1 Abstract

The muHVT package is a collection of R functions to facilitate building topology preserving maps for rich multivariate data analysis. Tending towards a big data preponderance, a large number of rows. A collection of R functions for this typical workflow is organized below:

Data Compression: Vector quantization (VQ), HVQ (hierarchical vector quantization) using means or medians. This step compresses the rows (long data frame) using a compression objective.
Data Projection: Dimension projection of the compressed cells to 1D,2D or 3D with the Sammons Non-linear Algorithm. This step creates topology preserving map (also called as embedding) coordinates into the desired output dimension.
Tessellation: Create cells required for object visualization using the Voronoi Tessellation method, package includes heatmap plots for hierarchical Voronoi tessellations (HVT). This step enables data insights, visualization, and interaction with the topology preserving map. Useful for semi-supervised tasks.
Prediction: Scoring new data sets and recording their assignment using the map objects from the above steps, in a sequence of maps if required.

X	price	speed	hd	ram	screen	cd	multi	premium	ads	trend
1	1499	25	80	4	14	no	no	yes	94	1
2	1795	33	85	2	14	no	no	yes	94	1
3	1595	25	170	4	15	no	no	yes	94	1
4	1849	25	170	8	14	no	no	no	94	1
5	3295	33	340	16	14	no	no	yes	94	1
6	3695	66	340	16	14	no	no	yes	94	1

Row.No	price	speed	hd	ram	screen	ads
2463	2799	50	230	8	15	216
2511	2197	33	270	4	14	216
2227	2744	50	340	8	17	275
526	2999	66	245	16	15	139
4291	1974	33	200	4	14	248
2986	2490	33	528	16	14	267

3 Map A : Base Compressed Map

Let us try to visualize the compressed Map A from the flow diagram below.

Figure 1: Flow map with highlighted bounding box in red around compressed map A

This package can perform vector quantization using the following algorithms -

Hierarchical Vector Quantization using k−means
Hierarchical Vector Quantization using k−medoids

For more information on vector quantization, refer the following link.

The HVT function constructs highly compressed hierarchical Voronoi tessellations. The raw data is first scaled and this scaled data is supplied as input to the vector quantization algorithm. The vector quantization algorithm compresses the dataset until a user-defined compression percentage/rate is achieved using a parameter called quantization error which acts as a threshold and determines the compression percentage. It means that for a given user-defined compression percentage we get the ‘n’ number of cells, then all of these cells formed will have a quantization error less than the threshold quantization error.

Let’s try to comprehend the HVT function first before moving ahead.

HVT(
  dataset,
  min_compression_perc,
  n_cells,
  depth,
  quant.err,
  distance_metric = c("L1_Norm", "L2_Norm"),
  error_metric = c("mean", "max"),
  quant_method = c("kmeans", "kmedoids"),
  normalize = TRUE,
  diagnose = FALSE,
  hvt_validation = FALSE,
  train_validation_split_ratio = 0.8
)

Each of the parameters have been explained below :

dataset - A dataframe with numeric columns. This is the input dataset on which the compression algorithm will be applied.
min_compression_perc - An integer indicating the minimum percent compression rate to be achieved for the dataset. The algorithm aims to compress the dataset while maintaining at least this specified compression percentage.
n_cells - An integer indicating the number of cells per hierarchy (level). This parameter determines the granularity or level of detail in the hierarchical vector quantization.
depth - An integer indicating the number of levels in the hierarchy. A depth of 1 means no hierarchy (single level), while higher values indicate multiple levels of hierarchy.
quant.error - A number indicating the quantization error threshold. A cell will only breakdown. into further cells if the quantization error of the cell is above the defined quantization error threshold.
projection.scale - A number indicating the scale factor for the tesselations so as to visualize the sub-tesselations. It helps in adjusting the visual representation of the hierarchy to make the sub-tesselations more visible.
scale_summary - A list with mean and standard deviation values for all the features in the dataset. Pass the scale summary when the input dataset is already scaled or normalize is set to False.
distance_metric - The distance metric can be L1_Norm or L2_Norm. L1_Norm is selected by default. The distance metric is used to calculate the distance between an n dimensional point and centroid. The user can also pass a custom function to calculate this distance.
error_metric - The error metric can be mean or max. max is selected by default. max will return the max of m values and mean will take mean of m values where each value is a distance between a point and centroid of the cell. Moreover, the user can also pass a custom function to calculate the error metric.
quant_method - The quantization method can be kmeans or kmedoids. kmeans is selected by default.
normalize - A logical value indicating whether the columns in your dataset need to be normalized. Default value is TRUE. The algorithm supports Z-score normalization.
diagnose - A logical value indicating whether user wants to perform diagnostics on the model. Default value is TRUE.
hvt_validation - A logical value indicating whether user wants to holdout a validation set and find mean absolute deviation of the validation points from the centroid. Default value is FALSE.
train_validation_split_ratio - A numeric value indicating train validation split ratio. This argument is only used when hvt_validation has been set to TRUE. Default value for the argument is 0.8.

We will use the HVT function to compress our data while preserving essential features of the dataset. Our goal is to achieve data compression upto atleast 80%. In situations where the compression ratio does not meet the desired target, we can explore adjusting the model parameters as a potential solution. This involves making modifications to parameters such as the quantization error threshold or increasing the number of cells and then rerunning the HVT function again.

In our example we will iteratively increase the number of cells until the desired compression percentage is reached instead of increasing the quantization threshold because it may reduce the level of detail captured in the data representation

First, we will construct map A by using the below mentioned model parameters.

3.0.1 Iteration 1:

We will pass the below mentioned model parameters along with training dataset (containing 5007 data poits) to HVT function.

Model Parameters

Number of Cells at each Level = 200
Maximum Depth = 1
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

set.seed(240)
map_A <- list()
map_A  <- muHVT::HVT(
  trainComputers,
  n_cells = 200,
  depth = 1,
  quant.err = 0.2,
  projection.scale = 10,
  normalize = T,
  distance_metric = "L1_Norm",
  error_metric = "max",
  quant_method = "kmeans"
)

Let’s checkout the compression summary with n_cells set to 200.

compressionSummaryTable(map_A[[3]]$compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	200	83	0.42	n_cells: 200 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 42% of cells have reached the quantization threshold error. Therefore we can further subdivide the cells by increasing the n_cells parameters and then see if desired compression (80%) is reached

3.0.2 Iteration 2:

Since, we are yet to achive atleast 80% compression. Let’s try to compress again using the below mentioned set of model parameters and the Computers training dataset (containing 5007 data points) .

Model Parameters

Number of Cells at each Level = 440
Maximum Depth = 1
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

map_A <- list()
map_A <-muHVT::HVT(trainComputers,
                n_cells = 440,
                quant.err = 0.2,
                depth = 1,
                distance_metric = "L1_Norm",
                error_metric = "max",
                quant_method = "kmeans",
                normalize = T)

As per the manual, map_A[[3]] gives us detailed information about the hierarchical vector quantized data. map_A[[3]][['summary']] gives a nice tabular data containing no of points, Quantization Error and the codebook.

The datatable displayed below is the summary from map A showing Cell.ID, Centroids and Quantization Error for each of the 440 cells.

summaryTable(map_A[[3]]$summary,scroll = T,limit = 500)

Segment.Level	Segment.Parent	Segment.Child	n	Cell.ID	Quant.Error	price	speed	hd	ram	screen	ads
1	1	1	7	46	0.08	-0.76	-0.89	-0.88	-0.76	-0.67	1.57
1	1	2	10	108	0.08	-0.80	-0.89	-0.16	-0.76	-0.67	0.67
1	1	3	15	223	0.12	0.37	-0.89	-0.72	-0.05	0.43	-1.65
1	1	4	11	54	0.07	-1.50	-0.89	-0.75	-0.76	-0.67	0.62
1	1	5	8	146	0.13	-0.31	0.68	-0.95	-0.89	-0.67	-0.14
1	1	6	11	150	0.16	-0.66	0.68	-0.78	-0.79	-0.67	-0.73
1	1	7	11	170	0.1	0.03	-1.24	-0.13	-0.05	-0.67	0.38
1	1	8	8	334	0.15	0.62	2.30	0.08	-0.05	0.43	0.04
1	1	9	8	114	0.07	-0.16	0.68	-1.19	-1.11	-0.67	0.87
1	1	10	7	248	0.17	0.51	-0.08	0.34	-0.05	-0.67	-0.30
1	1	11	9	140	0.12	-0.01	0.68	-1.15	-1.00	-0.67	0.33
1	1	12	7	219	0.14	-1.36	0.24	0.46	-0.05	-0.67	-0.74
1	1	13	9	271	0.05	-1.08	0.68	0.49	-0.05	0.43	-0.84
1	1	14	19	109	0.06	-0.31	-0.89	-0.74	-0.76	-0.67	0.38
1	1	15	6	176	0.08	-0.72	-0.89	-0.07	-0.05	0.43	0.72
1	1	16	17	332	0.14	0.42	2.30	0.10	-0.05	0.43	1.50
1	1	17	12	18	0.05	-1.21	-1.27	-1.19	-1.11	-0.67	0.97
1	1	18	19	149	0.16	-0.68	-0.08	-0.46	-0.76	0.43	0.79
1	1	19	17	428	0.35	0.18	2.30	2.53	1.37	0.43	-2.22
1	1	20	20	320	0.36	0.82	-0.16	-0.09	-0.12	2.64	0.71
1	1	21	3	305	0.18	2.27	-0.35	-0.01	-0.05	-0.67	-1.32
1	1	22	7	227	0.1	-0.51	-0.89	0.45	-0.05	0.43	-0.47
1	1	23	10	178	0.12	0.00	0.68	-0.86	-0.76	-0.67	-0.90
1	1	24	9	365	0.1	0.68	-0.08	1.20	1.37	0.43	-0.36
1	1	25	5	14	0.11	-1.99	-0.89	-0.96	-1.11	-0.67	0.18
1	1	26	3	411	0.05	1.25	-0.89	2.29	2.79	0.43	0.57
1	1	27	18	122	0.15	-0.18	-0.98	-0.85	-0.76	0.43	0.68
1	1	28	15	189	0.11	0.40	-0.92	0.03	-0.05	-0.67	0.87
1	1	29	11	107	0.11	-0.49	-0.96	-0.88	-0.76	-0.67	-0.64
1	1	30	7	423	0.47	3.55	0.12	2.51	1.37	-0.67	0.44
1	1	31	14	90	0.05	-0.63	-0.89	-0.79	-0.76	-0.67	0.58
1	1	32	22	430	0.24	0.63	0.75	3.07	2.79	0.43	-2.27
1	1	33	5	390	0.3	1.37	-0.89	3.73	-0.19	-0.45	0.70
1	1	34	25	101	0.18	-0.85	-0.97	-0.71	-0.76	0.43	0.84
1	1	35	11	425	0.07	0.15	2.30	1.70	1.37	0.43	-2.39
1	1	36	10	358	0.05	0.24	-0.89	1.20	1.37	0.43	-0.84
1	1	37	16	166	0.11	0.03	0.68	-1.08	-0.78	-0.67	-1.65
1	1	38	13	45	0.05	-0.91	-0.89	-1.19	-1.11	-0.67	0.42
1	1	39	8	383	0.12	1.15	2.30	0.45	1.37	-0.67	-0.16
1	1	40	5	9	0.07	-1.24	-0.97	-1.19	-1.11	-0.67	1.57
1	1	41	11	419	0.06	1.41	-0.89	2.29	2.79	0.43	-0.82
1	1	42	8	242	0.14	-0.81	-0.08	0.30	-0.05	0.43	-0.65
1	1	43	13	179	0.09	0.41	0.68	-0.76	-0.76	-0.67	0.40
1	1	44	5	375	0.04	0.06	-0.08	1.70	1.37	0.43	-0.79
1	1	45	20	129	0.14	-1.15	0.68	-0.79	-0.76	-0.67	-0.40
1	1	46	10	292	0.22	0.86	0.68	-0.65	-0.12	0.43	-1.41
1	1	47	5	79	0.12	-0.89	-1.04	-0.94	-0.05	-0.67	1.02
1	1	48	23	246	0.11	-0.42	0.68	0.46	-0.05	-0.67	-0.63
1	1	49	8	207	0.25	0.74	-0.89	-0.40	-0.40	0.43	0.52
1	1	50	11	27	0.06	-1.06	-1.27	-1.19	-1.11	-0.67	0.43
1	1	51	8	51	0.11	-1.23	-0.08	-1.05	-0.85	-0.67	0.94
1	1	52	19	288	0.09	0.81	-0.89	0.45	1.37	-0.67	0.88
1	1	53	7	154	0.1	-0.62	0.68	-0.15	-0.76	-0.67	0.84
1	1	54	10	261	0.15	0.61	-0.08	-0.67	-0.05	0.43	-1.40
1	1	55	10	195	0.15	0.18	0.77	-0.09	-0.76	-0.67	0.83
1	1	56	14	250	0.09	0.52	0.68	-0.69	-0.05	-0.67	-1.61
1	1	57	20	331	0.2	-0.63	0.68	0.30	-0.76	2.64	-0.95
1	1	58	9	379	0.15	1.33	-0.08	-0.65	-0.29	2.64	-1.52
1	1	59	14	11	0.21	-0.28	-1.05	-0.79	-0.76	2.64	0.53
1	1	60	29	359	0.13	1.36	0.68	0.18	1.37	0.43	0.77
1	1	61	6	337	0.1	2.46	0.68	0.21	-0.05	-0.67	-0.87
1	1	62	6	1	0.17	-0.17	-1.21	-1.02	-0.76	2.64	1.32
1	1	63	28	243	0.33	-0.33	2.30	-0.23	-0.46	-0.67	-0.88
1	1	64	8	274	0.07	0.41	-1.27	0.45	1.37	-0.67	0.68
1	1	65	13	362	0.14	1.07	0.75	0.35	1.37	0.43	1.34
1	1	66	10	143	0.07	-0.34	-0.89	-0.80	-0.05	-0.67	-1.66
1	1	67	4	265	0.05	-0.55	0.68	1.23	-0.05	-0.67	-0.69
1	1	68	11	13	0.15	-0.83	-0.89	-0.25	-0.76	2.64	-0.33
1	1	69	8	298	0.17	-0.62	0.20	2.29	-0.05	-0.67	-0.95
1	1	70	4	335	0.06	1.34	-0.08	0.45	1.37	-0.67	-0.08
1	1	71	20	204	0.16	0.09	-0.08	0.02	-0.05	-0.67	0.86
1	1	72	10	42	0.06	-1.49	-0.89	-0.75	-0.76	-0.67	1.04
1	1	73	1	429	0	3.08	0.68	0.04	4.20	0.43	0.71
1	1	74	14	186	0.14	-0.79	-0.89	0.45	-0.05	-0.67	-0.68
1	1	75	4	410	0.37	2.27	0.68	3.73	-0.23	-0.40	0.68
1	1	76	9	163	0.16	1.05	-0.89	-0.41	-0.60	-0.67	0.61
1	1	77	10	400	0.07	-0.03	0.68	1.70	1.37	0.43	-2.38
1	1	78	6	275	0.18	1.14	0.68	0.13	-0.05	-0.67	-0.18
1	1	79	25	241	0.16	-0.88	0.68	0.40	-0.05	-0.67	-1.06
1	1	80	6	245	0.14	-1.22	0.68	-0.30	-0.05	0.43	-0.91
1	1	81	21	120	0.16	-0.46	-0.08	-0.82	-0.62	-0.67	0.70
1	1	82	11	40	0.18	-0.93	-0.99	-1.19	-1.11	0.43	0.37
1	1	83	9	342	0.28	1.16	0.43	-0.52	-0.68	2.64	1.12
1	1	84	8	286	0.05	-0.72	1.11	0.50	-0.05	0.43	-0.83
1	1	85	8	33	0.1	-1.06	-0.89	-1.18	-1.02	-0.67	-0.99
1	1	86	5	282	0.23	-1.50	0.53	0.19	-0.48	0.43	-2.16
1	1	87	17	137	0.07	-0.31	0.68	-0.78	-0.76	-0.67	0.96
1	1	88	19	168	0.07	-0.08	-0.89	0.05	-0.05	-0.67	1.04
1	1	89	7	291	0.05	1.10	-0.89	0.15	1.37	-0.67	0.36
1	1	90	6	24	0.19	-0.97	-1.02	-1.07	-0.94	0.43	-1.40
1	1	91	4	434	0.59	4.07	1.49	1.29	0.30	2.64	0.07
1	1	92	19	409	0.65	0.92	0.42	1.41	1.37	2.64	-0.60
1	1	93	9	393	0.46	2.04	2.30	0.91	-0.05	-0.31	-0.34
1	1	94	8	22	0.08	-1.66	-1.27	-0.84	-0.76	-0.67	0.84
1	1	95	23	158	0.22	-1.41	0.68	-0.20	-0.70	-0.67	-1.05
1	1	96	11	330	0.22	1.55	0.68	-0.45	-0.31	0.43	-1.59
1	1	97	6	145	0.17	0.49	-0.89	-0.64	-0.76	-0.67	-0.17
1	1	98	12	121	0.15	0.21	-0.89	-0.80	-0.76	-0.67	-1.70
1	1	99	14	329	0.24	2.06	0.63	0.31	-0.25	0.43	0.99
1	1	100	16	299	0.05	1.21	-0.89	0.46	1.37	-0.67	0.86
1	1	101	5	328	0.1	-0.87	1.11	1.33	-0.05	0.43	-1.13
1	1	102	10	278	0.1	0.33	-0.93	0.45	1.37	-0.67	0.02
1	1	103	5	102	0.1	-1.05	0.68	-0.76	-0.76	-0.67	1.27
1	1	104	12	25	0.08	-1.25	-1.05	-0.78	-0.76	-0.67	1.57
1	1	105	9	385	0.25	2.05	0.34	-0.21	-0.05	2.64	1.12
1	1	106	10	231	0.18	-0.02	-0.08	0.02	-0.05	0.43	1.29
1	1	107	5	193	0.14	-0.66	-0.08	-0.33	-0.05	-0.67	-1.01
1	1	108	4	418	0.06	-0.03	0.68	3.07	1.37	0.43	-2.25
1	1	109	4	306	0.16	1.83	0.68	-0.59	-0.05	-0.67	-1.57
1	1	110	5	378	0.15	1.09	0.68	1.20	1.37	0.43	-0.11
1	1	111	2	308	0.07	-0.55	0.68	0.26	1.37	-0.67	-1.25
1	1	112	18	239	0.15	0.44	0.75	0.08	-0.05	-0.67	1.05
1	1	113	11	62	0.06	-1.14	-0.89	-0.81	-0.76	-0.67	0.73
1	1	114	5	169	0.07	-0.26	-0.89	0.47	-0.05	-0.67	1.40
1	1	115	18	415	0.11	0.74	-0.08	2.29	2.79	0.43	-0.93
1	1	116	5	97	0.13	0.23	-1.19	-1.05	-0.76	-0.67	0.77
1	1	117	24	19	0.22	-0.06	2.30	-0.89	-0.89	-0.67	1.18
1	1	118	9	184	0.17	-0.98	0.34	-0.29	-0.05	-0.67	-0.08
1	1	119	11	43	0.12	-0.82	-0.89	-1.04	-0.79	-0.67	-1.65
1	1	120	9	257	0.29	2.17	-0.62	0.10	-0.05	-0.55	0.65
1	1	121	27	155	0.18	0.15	0.68	-0.84	-0.76	-0.67	0.81
1	1	122	20	215	0.11	0.33	-0.08	-0.69	-0.05	-0.67	-1.64
1	1	123	14	348	0.24	0.61	0.74	0.37	1.37	0.43	0.25
1	1	124	13	162	0.12	0.01	-1.01	-0.67	-0.05	-0.67	-1.57
1	1	125	7	366	0.22	1.62	0.35	-0.29	1.37	-0.67	-1.65
1	1	126	9	77	0.02	-0.88	-0.89	-0.78	-0.76	-0.67	0.66
1	1	127	25	253	0.14	0.70	0.68	0.16	-0.05	-0.67	0.61
1	1	128	9	84	0.09	-0.27	-1.27	-0.81	-0.76	-0.67	0.81
1	1	129	3	398	0.04	-0.03	-0.08	2.29	1.37	0.43	-1.98
1	1	130	9	412	0.05	0.90	-0.89	2.29	2.79	0.43	-0.48
1	1	131	8	309	0.06	1.43	-0.89	0.41	1.37	-0.67	0.46
1	1	132	8	267	0.08	0.10	0.68	0.37	-0.05	0.43	1.57
1	1	133	16	252	0.22	0.94	-0.08	-0.40	-0.14	0.43	0.43
1	1	134	15	119	0.13	-0.60	-0.99	-0.75	-0.76	0.43	0.24
1	1	135	13	48	0.09	-0.66	-0.92	-1.19	-1.11	-0.67	0.82
1	1	136	9	326	0.11	1.45	-0.08	0.32	1.37	-0.67	0.38
1	1	137	11	386	0.22	1.37	0.68	-0.66	-0.18	2.64	-1.36
1	1	138	20	255	0.15	0.43	-0.89	-0.34	-0.05	2.64	0.68
1	1	139	8	161	0.14	-0.68	-0.94	-0.08	-0.05	0.43	1.41
1	1	140	10	38	0.13	-1.22	-1.19	-0.92	-0.76	0.43	0.91
1	1	141	14	180	0.12	0.09	0.68	-0.56	-0.76	-0.67	0.07
1	1	142	13	427	0.21	2.01	0.62	2.29	2.79	0.43	-0.18
1	1	143	15	354	0.13	1.63	0.68	0.39	1.37	-0.67	0.26
1	1	144	11	301	0.14	0.82	-1.03	0.15	1.37	-0.67	-0.85
1	1	145	7	205	0.13	0.24	-0.08	-0.05	-0.05	-0.67	1.57
1	1	146	10	327	0.14	0.66	0.68	0.60	1.37	-0.67	0.42
1	1	147	11	403	0.11	0.92	2.30	1.22	1.37	0.43	-0.79
1	1	148	7	127	0.08	-0.20	-0.89	-0.10	-0.76	-0.67	0.69
1	1	149	6	217	0.11	0.41	-0.89	0.26	-0.05	-0.67	-0.34
1	1	150	11	323	0.1	0.96	-0.89	0.46	1.37	0.43	0.72
1	1	151	9	208	0.16	0.16	0.68	-0.60	-0.05	-0.67	0.43
1	1	152	14	405	0.14	-0.04	0.68	2.29	1.37	0.43	-2.24
1	1	153	10	98	0.08	-0.22	-0.89	-1.14	-0.76	-0.67	0.33
1	1	154	7	47	0.05	-0.99	-0.89	-1.16	-0.76	-0.67	1.03
1	1	155	8	182	0.15	-1.00	0.68	0.08	-0.76	-0.67	-0.46
1	1	156	9	153	0.07	-0.24	-0.93	0.04	-0.05	-0.67	1.57
1	1	157	23	65	0.08	-1.23	-0.89	-0.82	-0.76	-0.67	0.27
1	1	158	4	367	0.08	3.03	-0.08	0.15	-0.05	-0.67	-1.65
1	1	159	29	421	0.15	1.04	0.68	2.29	2.79	0.43	-0.84
1	1	160	10	111	0.14	-1.33	-0.89	0.06	-0.76	-0.67	-0.65
1	1	161	13	382	0.25	3.03	0.68	0.23	-0.05	-0.42	-1.68
1	1	162	10	142	0.1	-0.54	-0.89	-0.76	-0.05	-0.67	-0.73
1	1	163	14	433	0.32	1.31	-0.10	2.29	2.79	2.64	-0.81
1	1	164	14	50	0.08	-0.88	-0.08	-1.19	-1.11	-0.67	0.91
1	1	165	23	125	0.14	-0.78	0.68	-0.84	-0.76	-0.67	0.41
1	1	166	7	67	0.09	-0.95	0.68	-1.13	-0.96	-0.67	1.03
1	1	167	12	254	0.11	-0.53	0.68	0.02	-0.05	0.43	-0.24
1	1	168	11	113	0.17	-0.63	-0.08	-0.98	-0.92	-0.67	-0.30
1	1	169	9	83	0.04	-0.71	-0.89	-0.75	-0.76	-0.67	0.76
1	1	170	10	89	0.11	-1.18	-0.93	-0.19	-0.76	-0.67	0.66
1	1	171	8	44	0.06	-1.41	-0.89	-1.13	-0.76	-0.67	0.36
1	1	172	8	310	0.08	1.13	-0.99	0.45	1.37	-0.67	-0.08
1	1	173	8	6	0.08	-1.21	-0.89	-1.28	-1.11	-0.67	-1.64
1	1	174	14	295	0.12	-0.78	-0.08	0.38	-0.76	2.64	-1.00
1	1	175	12	316	0.19	1.00	-1.02	-0.29	1.37	-0.67	-1.61
1	1	176	2	346	0.15	0.38	-0.11	2.60	-0.05	0.43	0.69
1	1	177	12	194	0.17	-0.92	0.68	-0.46	-0.76	0.43	-0.16
1	1	178	17	396	0.19	1.51	2.30	0.45	1.37	-0.09	1.34
1	1	179	15	206	0.13	-1.30	-0.08	0.26	-0.76	0.43	-0.90
1	1	180	4	322	0.13	1.35	-0.89	-0.06	1.37	0.43	0.52
1	1	181	31	225	0.12	-0.25	0.68	0.14	-0.05	-0.67	0.21
1	1	182	5	185	0.14	0.46	-0.57	-0.29	-0.76	0.43	1.27
1	1	183	12	103	0.15	-0.68	-0.08	-0.63	-0.76	-0.67	1.39
1	1	184	2	151	0.06	-0.29	-0.89	0.06	-0.76	-0.67	-0.87
1	1	185	11	232	0.12	-0.39	0.68	0.22	-0.05	-0.67	-0.20
1	1	186	9	313	0.29	0.41	-0.89	1.32	1.37	-0.67	0.31
1	1	187	17	372	0.15	0.23	0.70	1.21	1.37	0.43	-0.74
1	1	188	9	407	0.09	0.41	2.30	1.70	1.37	0.43	-1.08
1	1	189	14	68	0.36	-0.83	0.19	-1.16	-1.09	0.43	0.95
1	1	190	9	37	0.08	-1.53	-1.27	-0.83	-0.76	-0.67	0.41
1	1	191	8	28	0.13	-1.33	-1.22	-1.10	-0.85	-0.67	-0.62
1	1	192	8	71	0.24	-1.65	-0.89	-0.19	-0.67	-0.67	0.43
1	1	193	9	7	0.2	-0.89	-0.89	-0.36	-0.84	2.64	0.50
1	1	194	16	53	0.05	-1.15	-0.89	-0.82	-0.76	-0.67	1.06
1	1	195	9	29	0.07	-0.60	-1.27	-1.08	-0.76	-0.67	-1.66
1	1	196	14	377	0.21	2.10	0.68	0.25	1.37	0.43	0.77
1	1	197	10	190	0.2	-0.45	-0.73	-0.74	-0.05	0.43	-0.77
1	1	198	11	15	0.13	-1.32	-1.03	-1.19	-1.11	0.43	0.88
1	1	199	13	344	0.23	0.11	0.68	0.14	-0.05	2.64	-0.39
1	1	200	12	128	0.1	0.05	-0.08	-0.81	-0.76	-0.67	0.90
1	1	201	17	36	0.06	-1.25	-1.27	-1.13	-0.76	-0.67	0.40
1	1	202	12	172	0.1	-0.33	-0.89	-0.44	-0.05	0.43	0.94
1	1	203	8	293	0.04	-0.74	1.11	0.50	-0.05	0.43	-1.22
1	1	204	4	165	0.06	0.02	-0.89	-0.67	-0.76	0.43	-1.69
1	1	205	13	187	0.13	0.43	-0.89	-0.71	-0.05	-0.67	-1.63
1	1	206	4	210	0.13	0.00	2.30	-0.78	-0.76	0.43	0.94
1	1	207	12	70	0.05	-0.72	-1.27	-0.81	-0.76	-0.67	0.69
1	1	208	13	156	0.14	0.14	-0.08	-0.21	-0.76	-0.67	0.93
1	1	209	10	317	0.17	0.87	2.30	0.21	-0.05	-0.67	1.31
1	1	210	19	197	0.23	-0.12	0.68	-0.79	-0.83	0.43	0.37
1	1	211	13	201	0.15	0.33	-0.98	-0.29	-0.05	-0.67	-0.85
1	1	212	12	94	0.15	-1.25	-0.08	-0.80	-0.76	-0.67	0.38
1	1	213	26	85	0.09	-0.35	-0.89	-0.98	-0.76	-0.67	-1.63
1	1	214	13	324	0.11	0.51	2.30	0.21	-0.05	0.43	0.66
1	1	215	9	95	0.08	-0.54	0.68	-1.18	-1.07	-0.67	1.01
1	1	216	23	126	0.14	-0.21	-0.08	-1.01	-0.77	-0.67	-1.64
1	1	217	10	256	0.2	0.88	0.68	-0.39	-0.05	-0.67	-0.79
1	1	218	19	356	0.34	-0.96	0.52	1.97	-0.20	-0.67	-2.18
1	1	219	14	147	0.11	0.17	-0.08	-0.77	-0.76	-0.67	0.27
1	1	220	14	240	0.2	0.44	0.86	0.03	-0.10	-0.67	1.57
1	1	221	13	191	0.18	0.14	-0.08	-0.75	-0.49	0.43	0.67
1	1	222	7	380	0.05	0.04	-0.08	1.70	1.37	0.43	-1.23
1	1	223	18	88	0.05	-0.46	-0.89	-0.77	-0.76	-0.67	1.00
1	1	224	12	417	0.1	1.69	0.68	2.29	2.79	-0.67	0.38
1	1	225	18	413	0.27	2.56	0.68	-0.04	1.37	2.64	0.59
1	1	226	9	401	0.08	0.81	-0.89	1.20	1.37	2.64	-0.61
1	1	227	8	49	0.08	-1.28	-0.89	-0.29	-0.76	-0.67	1.41
1	1	228	8	12	0.18	-1.32	-1.03	-0.93	-0.85	0.43	1.57
1	1	229	6	237	0.19	0.49	0.17	-0.73	-0.76	0.43	-1.52
1	1	230	16	266	0.12	-0.03	0.68	0.49	-0.05	0.43	0.54
1	1	231	5	82	0.12	-1.25	-1.12	-0.29	-0.05	-0.67	1.27
1	1	232	24	110	0.12	-0.42	-1.00	-0.71	-0.76	-0.67	-0.07
1	1	233	14	270	0.15	0.34	-1.11	0.45	1.37	-0.67	1.39
1	1	234	4	200	0.07	-0.30	-0.89	0.26	-0.05	0.43	1.57
1	1	235	16	263	0.29	0.70	0.35	-0.52	-0.18	0.43	-0.62
1	1	236	12	280	0.21	1.35	0.68	-0.41	-0.23	0.43	0.90
1	1	237	8	281	0.07	-0.96	0.68	0.48	-0.05	0.43	-1.23
1	1	238	7	287	0.13	0.20	0.68	0.45	-0.05	0.43	-0.55
1	1	239	16	116	0.18	-1.23	-0.08	-0.57	-0.76	-0.67	-0.37
1	1	240	31	321	0.42	-0.50	2.30	0.40	-0.12	-0.03	-1.00
1	1	241	16	420	0.13	1.51	-0.08	2.29	2.79	0.43	-0.49
1	1	242	8	199	0.12	0.05	-1.03	0.20	-0.05	-0.67	-0.07
1	1	243	8	314	0.07	0.99	-0.08	0.45	1.37	-0.67	0.72
1	1	244	7	289	0.06	-0.20	0.68	1.23	-0.05	0.43	0.35
1	1	245	15	216	0.16	-0.16	0.68	0.03	-0.05	-0.67	0.76
1	1	246	14	273	0.16	0.31	0.63	0.14	-0.05	0.43	0.07
1	1	247	20	132	0.18	-0.76	-0.08	-0.14	-0.72	-0.67	0.74
1	1	248	5	369	0.08	-0.74	0.68	1.70	-0.05	0.43	-2.35
1	1	249	11	432	0.33	0.40	1.35	2.13	1.37	2.64	-2.28
1	1	250	6	341	0.22	0.46	0.62	0.21	1.37	0.43	1.36
1	1	251	24	404	0.12	1.30	-0.89	2.29	2.79	-0.67	0.33
1	1	252	9	63	0.05	-1.23	-0.89	-0.75	-0.76	-0.67	0.65
1	1	253	11	226	0.16	0.32	-0.96	-0.44	-0.05	0.43	-0.89
1	1	254	11	133	0.09	-0.43	-0.89	-0.75	-0.05	-0.67	0.33
1	1	255	19	376	0.16	0.60	0.68	1.17	1.37	0.43	-0.70
1	1	256	8	80	0.06	-0.23	-0.89	-1.15	-0.76	-0.67	0.91
1	1	257	13	164	0.27	-0.58	0.15	-0.89	-0.87	0.43	-0.58
1	1	258	7	78	0.07	-0.82	-0.89	-0.29	-0.76	-0.67	1.33
1	1	259	5	422	0.05	1.83	-0.89	2.29	2.79	0.43	-0.23
1	1	260	10	23	0.11	-1.45	-1.23	-1.16	-0.83	-0.67	-0.10
1	1	261	8	60	0.05	-0.57	-1.27	-1.14	-0.76	-0.67	0.44
1	1	262	12	93	0.12	-0.88	-0.92	-0.78	-0.76	-0.67	-0.41
1	1	263	20	279	0.24	0.33	2.30	0.14	-0.19	-0.67	0.31
1	1	264	15	75	0.05	-0.67	-0.89	-1.10	-0.76	-0.67	0.39
1	1	265	15	351	0.04	0.27	-0.89	1.20	1.37	0.43	-0.45
1	1	266	7	408	0.25	2.71	-0.89	-0.27	1.37	2.64	0.06
1	1	267	10	57	0.07	-0.55	-1.27	-1.15	-0.76	-0.67	0.82
1	1	268	13	387	0.24	0.11	0.68	1.48	-0.05	2.64	-0.61
1	1	269	6	436	0.27	1.23	2.30	1.91	2.08	2.64	-1.04
1	1	270	11	202	0.09	0.20	-0.89	0.47	-0.05	-0.67	0.68
1	1	271	12	402	0.47	1.54	0.81	2.42	1.37	0.34	0.54
1	1	272	12	152	0.15	-0.05	-0.08	-0.83	-0.76	-0.67	-0.87
1	1	273	9	21	0.09	-1.48	-0.89	-1.15	-0.96	-0.67	0.95
1	1	274	11	69	0.05	-0.74	-0.89	-0.84	-0.76	-0.67	1.11
1	1	275	14	249	0.24	-0.06	-0.77	-0.01	-0.15	2.64	0.62
1	1	276	4	318	0.08	-1.02	0.68	1.35	-0.05	0.43	-1.21
1	1	277	19	247	0.15	0.09	0.68	-0.36	-0.05	0.43	0.65
1	1	278	14	297	0.17	1.23	0.68	0.23	-0.05	0.43	0.58
1	1	279	8	338	0.08	0.88	0.68	0.45	1.37	-0.67	1.41
1	1	280	13	171	0.1	-0.38	-0.92	0.00	-0.05	-0.67	0.06
1	1	281	14	371	0.28	-0.93	2.30	0.46	-0.20	-0.12	-2.31
1	1	282	24	173	0.33	-0.28	0.73	-0.75	-0.82	0.43	0.97
1	1	283	18	355	0.14	1.38	0.68	-0.21	1.37	0.43	0.42
1	1	284	4	345	0.11	0.83	-0.49	-0.66	1.37	0.43	-1.59
1	1	285	11	56	0.05	-0.83	-1.27	-0.78	-0.76	-0.67	1.01
1	1	286	7	370	0.19	1.55	0.68	-0.38	1.37	0.43	-1.02
1	1	287	10	64	0.06	-0.48	-0.89	-1.19	-1.11	-0.67	0.41
1	1	288	9	167	0.15	-0.36	-0.89	-0.71	-0.05	0.43	0.29
1	1	289	6	124	0.15	-1.28	-1.02	-0.29	-0.05	-0.67	-0.30
1	1	290	14	272	0.11	-0.70	0.68	0.43	-0.05	0.43	-0.78
1	1	291	25	188	0.09	0.21	-0.89	0.05	-0.05	-0.67	0.52
1	1	292	23	435	0.3	1.05	2.30	2.59	2.79	0.43	-1.56
1	1	293	8	8	0.08	-1.66	-1.27	-1.18	-0.94	-0.67	1.07
1	1	294	14	431	0.2	2.14	2.30	2.29	2.79	-0.60	0.40
1	1	295	3	96	0.08	-0.16	-0.89	-0.58	-0.76	-0.67	1.57
1	1	296	9	87	0.06	-0.46	-1.27	-0.78	-0.76	-0.67	0.39
1	1	297	6	414	0.04	0.90	-0.89	2.29	2.79	0.43	-0.85
1	1	298	10	230	0.18	0.31	-0.89	0.32	-0.12	0.43	0.71
1	1	299	13	58	0.11	-0.84	-0.95	-1.16	-0.98	-0.67	-0.08
1	1	300	11	4	0.31	0.63	-1.03	-0.85	-0.76	2.64	0.96
1	1	301	24	277	0.18	0.59	0.68	0.21	-0.08	0.43	0.76
1	1	302	13	130	0.1	-0.85	-0.08	-0.61	-0.76	-0.67	-0.80
1	1	303	6	131	0.17	-0.53	0.68	-0.61	-0.52	-0.67	1.57
1	1	304	10	55	0.09	-1.09	-1.27	-0.81	-0.76	-0.67	0.53
1	1	305	21	269	0.11	-1.16	0.68	0.48	-0.05	-0.67	-2.25
1	1	306	21	91	0.1	-0.45	-0.08	-1.19	-1.11	-0.67	0.57
1	1	307	9	104	0.08	-0.46	-0.98	-0.87	-0.76	-0.67	-1.12
1	1	308	19	32	0.07	-1.02	-0.89	-1.19	-1.11	-0.67	0.97
1	1	309	4	268	0.13	0.22	-0.89	0.07	-0.05	2.64	1.46
1	1	310	4	59	0.03	-0.93	-0.89	-1.14	-0.76	-0.67	0.69
1	1	311	4	2	0.12	-0.52	-0.89	-0.66	-0.76	2.64	1.22
1	1	312	8	3	0.15	-1.66	-1.22	-1.08	-0.89	-0.67	1.57
1	1	313	8	141	0.14	-0.61	-0.89	-0.21	-0.76	0.43	0.62
1	1	314	14	183	0.08	-0.35	-0.89	0.45	-0.05	-0.67	0.46
1	1	315	6	192	0.15	0.62	-0.89	-0.58	-0.76	0.43	-0.26
1	1	316	6	196	0.17	-0.81	-0.76	-0.07	-0.05	0.43	0.02
1	1	317	15	92	0.04	-0.75	-0.89	-0.75	-0.76	-0.67	0.36
1	1	318	6	214	0.21	1.06	0.17	-0.90	-0.64	-0.67	-1.66
1	1	319	9	389	0.05	0.14	0.68	1.70	1.37	0.43	-1.23
1	1	320	8	381	0.26	-0.31	2.30	1.86	-0.05	-0.12	-0.99
1	1	321	2	438	0.38	2.15	1.49	6.57	1.37	0.43	-0.28
1	1	322	18	224	0.09	-0.62	0.68	0.03	-0.05	-0.67	-0.65
1	1	323	7	361	0.05	0.63	-0.89	1.20	1.37	0.43	-0.72
1	1	324	8	352	0.12	0.50	-0.89	1.20	1.37	0.43	-0.08
1	1	325	8	234	0.1	-0.43	0.68	-0.12	-0.05	0.43	1.46
1	1	326	4	17	0.04	-1.04	-0.08	-1.19	-1.11	-0.67	1.57
1	1	327	3	5	0.04	-2.07	-0.89	-1.18	-1.11	-0.67	0.90
1	1	328	9	304	0.1	-0.22	0.68	1.23	-0.05	0.43	-0.46
1	1	329	9	350	0.09	-1.17	2.30	0.45	-0.76	-0.67	-2.26
1	1	330	12	294	0.1	0.98	-0.92	0.45	1.37	-0.67	0.42
1	1	331	9	307	0.16	0.42	-1.14	0.45	1.37	0.43	1.02
1	1	332	9	439	0.49	1.48	1.28	4.62	3.89	0.92	-2.46
1	1	333	10	148	0.16	-1.03	-0.57	-0.22	-0.76	0.43	-0.12
1	1	334	6	424	0.25	1.52	2.30	0.32	1.37	2.64	0.77
1	1	335	19	52	0.13	-1.61	-0.91	-0.75	-0.78	-0.67	-0.31
1	1	336	12	31	0.05	-1.28	-0.89	-1.19	-1.11	-0.67	0.60
1	1	337	19	123	0.2	-1.52	-0.08	-0.20	-0.76	-0.67	-0.94
1	1	338	11	218	0.17	-1.37	0.54	0.45	-0.76	-0.67	-2.18
1	1	339	18	262	0.2	0.88	0.68	-0.51	-0.17	0.43	0.51
1	1	340	3	325	0.07	-1.02	0.68	0.58	-0.05	0.43	-2.46
1	1	341	7	212	0.1	-1.39	0.68	-0.26	-0.76	0.43	-1.09
1	1	342	12	41	0.1	-1.75	-0.92	-0.62	-0.76	-0.67	-0.88
1	1	343	7	290	0.08	0.87	-1.27	0.46	1.37	-0.67	0.86
1	1	344	8	139	0.12	-1.50	-0.89	0.01	-0.76	0.43	-0.63
1	1	345	14	360	0.27	2.68	0.68	0.44	-0.25	0.43	0.22
1	1	346	6	181	0.05	-0.41	-0.89	-0.29	-0.05	0.43	0.55
1	1	347	13	353	0.11	1.54	0.68	0.47	1.37	-0.67	0.87
1	1	348	8	349	0.13	1.34	0.30	0.15	1.37	-0.67	-0.87
1	1	349	6	61	0.04	-0.65	-0.89	-1.14	-0.76	-0.67	0.94
1	1	350	21	336	0.25	0.45	0.68	0.04	-0.12	2.64	0.86
1	1	351	12	235	0.26	-0.42	0.36	-0.39	-0.76	2.64	0.12
1	1	352	10	115	0.16	-1.06	-1.08	-0.38	-0.05	-0.67	0.60
1	1	353	5	339	0.15	-0.28	0.22	2.29	-0.05	0.43	-0.55
1	1	354	16	397	0.34	-0.72	2.30	1.86	-0.14	-0.26	-2.30
1	1	355	4	236	0.15	-0.45	0.68	-0.08	-0.05	-0.67	-1.43
1	1	356	11	160	0.1	-0.31	-1.27	0.12	-0.05	-0.67	0.58
1	1	357	15	10	0.12	-1.28	-1.27	-1.05	-0.83	-0.67	-1.64
1	1	358	4	392	0.2	0.97	2.30	0.54	1.37	0.43	0.28
1	1	359	10	144	0.11	0.46	-0.89	-0.29	-0.76	-0.67	0.75
1	1	360	7	213	0.07	-0.32	-0.89	0.48	-0.05	0.43	0.59
1	1	361	9	394	0.28	2.75	0.68	-0.09	-0.29	2.64	0.44
1	1	362	7	340	0.2	0.97	-1.00	0.03	1.37	0.43	-0.83
1	1	363	21	251	0.24	1.15	0.68	-0.21	-0.12	-0.67	0.79
1	1	364	11	118	0.1	0.14	-0.89	-0.84	-0.76	-0.67	0.42
1	1	365	12	99	0.04	-0.35	-0.89	-0.79	-0.76	-0.67	0.76
1	1	366	8	388	0.15	1.15	2.30	0.73	1.37	-0.67	0.52
1	1	367	8	260	0.14	0.41	0.68	0.40	-0.05	-0.67	-0.36
1	1	368	12	117	0.09	-0.93	-0.89	-0.04	-0.76	-0.67	0.01
1	1	369	12	303	0.05	-0.52	0.68	1.23	-0.05	0.43	-0.83
1	1	370	7	395	0.21	1.06	2.30	0.16	-0.05	2.64	0.75
1	1	371	17	81	0.13	-1.49	-0.91	-0.30	-0.76	-0.67	-0.20
1	1	372	10	209	0.28	0.96	0.60	-0.64	-0.76	-0.67	-0.04
1	1	373	17	66	0.08	-1.33	-0.89	-0.81	-0.76	-0.67	-0.51
1	1	374	14	136	0.12	-0.28	-0.92	-0.50	-0.05	-0.67	1.05
1	1	375	20	198	0.09	-0.42	-0.89	0.45	-0.05	-0.67	-0.41
1	1	376	16	26	0.08	-1.40	-1.27	-1.15	-0.76	-0.67	0.81
1	1	377	7	343	0.22	0.87	-0.89	-0.65	-0.35	2.64	-1.45
1	1	378	10	39	0.06	-1.04	-1.27	-1.11	-0.76	-0.67	0.91
1	1	379	8	229	0.09	0.17	0.68	-0.63	-0.05	-0.67	-0.85
1	1	380	15	175	0.14	0.41	-0.89	-0.46	-0.05	-0.67	0.41
1	1	381	13	35	0.07	-1.28	-1.27	-0.82	-0.76	-0.67	1.03
1	1	382	14	220	0.09	-0.81	-0.08	0.45	-0.05	-0.67	-0.96
1	1	383	11	283	0.12	0.38	2.30	-0.05	-0.05	-0.67	1.46
1	1	384	10	134	0.12	-0.09	1.02	-0.78	-0.76	-0.67	1.36
1	1	385	14	416	0.08	1.33	-0.89	2.29	2.79	0.43	-0.30
1	1	386	15	76	0.18	-1.35	-0.94	-0.63	-0.81	0.43	0.43
1	1	387	23	157	0.08	-0.37	-0.89	-0.04	-0.05	-0.67	0.67
1	1	388	4	391	0.26	1.95	0.49	0.42	-0.05	2.64	0.27
1	1	389	6	105	0.08	-0.68	0.68	-1.25	-1.11	-0.67	-1.44
1	1	390	8	315	0.16	0.60	-1.08	0.37	1.37	0.43	0.25
1	1	391	14	72	0.04	-0.78	-0.89	-0.76	-0.76	-0.67	0.96
1	1	392	8	259	0.15	0.35	0.79	-0.12	-0.05	0.43	1.28
1	1	393	18	222	0.1	-0.38	-0.08	0.45	-0.05	-0.67	-0.53
1	1	394	11	333	0.06	1.49	-0.08	0.46	1.37	-0.67	0.89
1	1	395	15	276	0.1	-0.28	0.68	0.32	-0.05	0.43	-0.74
1	1	396	9	16	0.11	-1.53	-1.27	-1.19	-1.03	-0.67	0.53
1	1	397	9	364	0.08	0.27	-0.08	1.20	1.37	0.43	-0.75
1	1	398	10	211	0.14	0.00	-1.00	-0.05	-0.05	0.43	0.18
1	1	399	7	384	0.25	0.30	0.74	1.76	1.37	0.43	-0.80
1	1	400	6	177	0.14	1.15	-0.08	-0.61	-0.76	-0.67	0.67
1	1	401	19	203	0.11	-0.13	-1.01	0.05	-0.05	0.43	0.76
1	1	402	8	312	0.09	0.73	-0.08	0.45	1.37	-0.67	1.41
1	1	403	9	258	0.2	-0.18	-0.89	2.29	-0.05	-0.67	-0.38
1	1	404	11	244	0.16	-1.09	0.68	0.39	-0.76	0.43	-0.94
1	1	405	11	319	0.21	0.03	0.54	2.29	-0.05	-0.67	-0.36
1	1	406	15	159	0.08	-0.10	-1.27	0.08	-0.05	-0.67	0.91
1	1	407	33	221	0.14	0.64	-0.08	-0.05	-0.05	-0.67	0.68
1	1	408	11	112	0.09	-0.50	0.68	-1.18	-1.08	-0.67	0.48
1	1	409	17	399	0.31	1.42	0.73	0.26	1.37	2.64	0.75
1	1	410	5	20	0.08	-1.28	-1.27	-1.05	-0.90	-0.67	-1.12
1	1	411	6	34	0.08	-0.85	-0.08	-1.25	-1.11	-0.67	-1.44
1	1	412	6	426	0.2	3.36	0.68	-0.27	1.37	2.64	-0.22
1	1	413	12	285	0.1	0.72	-1.24	0.30	1.37	-0.67	0.32
1	1	414	8	363	0.08	1.28	1.11	0.45	1.37	-0.67	1.36
1	1	415	8	437	0.27	1.02	1.00	3.07	2.79	2.64	-2.30
1	1	416	7	284	0.19	0.20	-0.66	-0.74	-0.05	2.64	-0.71
1	1	417	9	264	0.06	0.29	-0.89	0.45	1.37	-0.67	0.66
1	1	418	21	238	0.21	0.24	-0.08	-0.09	-0.05	0.43	0.69
1	1	419	3	296	0.02	-0.47	0.68	0.03	-0.05	0.43	-2.34
1	1	420	10	100	0.16	-0.44	-0.97	-0.87	-0.76	0.43	1.21
1	1	421	11	174	0.15	-0.61	0.68	-0.02	-0.76	-0.67	0.17
1	1	422	9	311	0.25	1.52	-0.53	-0.55	-0.76	2.64	0.36
1	1	423	6	135	0.09	-0.58	-1.27	0.12	-0.05	-0.67	1.29
1	1	424	5	368	0.15	2.37	0.68	0.36	1.37	-0.67	0.68
1	1	425	9	373	0.12	1.65	0.68	0.39	1.37	0.43	-0.01
1	1	426	10	228	0.13	0.42	-0.08	-0.33	-0.05	-0.67	-0.92
1	1	427	20	86	0.22	-1.23	-0.93	-0.85	-0.83	0.43	-0.46
1	1	428	7	106	0.06	-0.07	-0.89	-0.76	-0.76	-0.67	0.98
1	1	429	8	138	0.12	-0.45	-0.99	-0.78	-0.76	0.43	-0.77
1	1	430	8	30	0.12	-0.51	1.05	-1.19	-1.11	-0.67	1.36
1	1	431	19	357	0.16	1.03	0.68	-0.27	-0.05	2.64	0.61
1	1	432	20	406	0.38	0.02	2.30	0.99	-0.05	2.64	-1.12
1	1	433	10	302	0.26	1.83	0.53	-0.09	-0.33	0.43	0.24
1	1	434	5	73	0.04	-0.72	-0.89	-0.95	-0.76	-0.67	0.82
1	1	435	1	440	0	5.51	0.68	3.07	4.20	2.64	0.50
1	1	436	14	300	0.29	2.04	0.68	0.38	-0.15	-0.67	0.69
1	1	437	19	74	0.1	-1.10	-0.89	-0.81	-0.76	-0.67	-0.90
1	1	438	7	347	0.13	1.21	-0.08	0.41	1.37	0.43	0.59
1	1	439	31	374	0.28	-0.27	0.87	0.49	-0.05	2.64	-1.24
1	1	440	17	233	0.2	-0.48	0.68	-0.10	-0.13	0.43	0.60

Now let us understand what each column in the above table means:

Segment.Level - Level of the cell. In this case, we have performed Vector Quantization for depth 1. Hence Segment Level is 1.
Segment.Parent - Parent segment of the cell.
Segment.Child (Cell.Number) - The children of a particular cell. In this case, it is the total number of cells at which we achieved the defined compression percentage.
n - No of points in each cell.
Cell.ID - Cell_ID’s are generated for the multivariate data using 1-D Sammon’s Projection algorithm.
Quant.Error - Quantization Error for each cell.

All the columns after this will contain centroids for each cell. They can also be called a codebook, which represents a collection of all centroids or codewords.

Now, let’s check the compression summary for HVT (map A) where n_cell was set to 440. The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

mapA_compression_summary <- map_A[[3]]$compression_summary %>%  dplyr::mutate_if(is.numeric, funs(round(.,4)))
compressionSummaryTable(mapA_compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	440	355	0.81	n_cells: 440 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 81% of the cells have hit the quantization threshold error.Since we are successfully able to attain the desired compression percentage, so we will not further subdivide the cells

Now let’s try to understand plotHVT function. The parameters have been explained in detail below:

plotHVT(hvt.results, line.width, color.vec, pch1 = 21, centroid.size = 3, title = NULL, maxDepth = 1)

hvt.results - A list containing the output of the HVT function which has the details of the tessellations to be plotted.
line.width - A vector indicating the line widths of the tessellation boundaries for each layer.
color.vec - A vector indicating the colors of the tessellations boundaries at each layer.
pch1 - Symbol, It plots the centroids with a particular symbol such as (solid circle, bullet, filled square, filled diamond) in the tessellations (parent levels).(default = 21 i.e filled circle).
centroid.size - Size of centroids of first level tessellations (default = 3).
title - Set a title for the plot (default = NULL).

Let’s plot the Voronoi tessellation for layer 1 (map A).

muHVT::plotHVT(map_A,
        line.width = c(0.4), 
        color.vec = c("#141B41"),
        centroid.size = 0.01,
        maxDepth = 1)

Figure 2: The Voronoi Tessellation for layer 1 (map A) shown for the 440 cells in the dataset ’computers’

Heat Maps

We will now overlay all the features as heatmap over the Voronoi Tessellation plot for better visualization and identification of patterns, trends, and variations in the data.

Let’s have a look at the function hvtHmap that we will use to overlay features as heatmap.

hvtHmap(hvt.results, dataset, child.level, hmap.cols, color.vec ,line.width, palette.color = 6)

hvt.results - A list of results obtained from the HVT function (map_A).
dataset - A dataframe containing the variables to overlay as a heatmap. The user can pass an external dataset or the dataset that was used to perform hierarchical vector quantization. The dataset should have the same number of points as the dataset used to perform hierarchical Vector Quantization in the HVT function.
child.level - A number indicating the level for which the heat map is to be plotted.
hmap.cols - The column number of column name from the dataset indicating the variables for which the heat map is to be plotted. To plot the quantization error as heatmap, pass 'quant_error'. Similarly to plot the no of points in each cell as heatmap, pass 'no_of_points' as a parameter.
color.vec - A color vector such that length(color.vec) = child.level, let say if the child.level is 2 then 2 color vector should be provided for each level. (default = NULL).
line.width - A line width vector such that length(line.width) = child.level, let say if the child.level is 2 then 2 line width vector should be provided for each level (default = NULL).
palette.color - A number indicating the heat map color palette. 1 - rainbow, 2 - heat.colors, 3 - terrain.colors, 4 - topo.colors, 5 - cm.colors, 6 - BlCyGrYlRd (Blue,Cyan,Green,Yellow,Red) color (default = 6).
show.points - A boolean indicating whether the centroids should be plotted on the tessellations (default = FALSE).

Now let’s plot the Voronoi Tessellation with the heatmap overlaid for all the features in the torus data for better visualization and interpretation of data patterns and distributions.

The heatmaps displayed below provides a visual representation of the spatial characteristics of the computers data, allowing us to observe patterns and trends in the distribution of each of the features (n,price,speed,hd,ram,screen,ads). The sheer green shades highlight regions with higher values in each of the heatmaps, while the indigo shades indicate areas with the lowest values in each of the heatmaps. By analyzing these heatmaps, we can gain insights into the variations and relationships between each of these features within the computers data.


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "n",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 3: The Voronoi Tessellation with the heat map overlaid for No. of entities in each cell


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "price",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 4: The Voronoi Tessellation with the heat map overlaid for variable ’price’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "speed",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 5: The Voronoi Tessellation with the heat map overlaid for variable ’speed’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "hd",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 6: The Voronoi Tessellation with the heat map overlaid for variable ’hd’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "ram",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 7: The Voronoi Tessellation with the heat map overlaid for variable ’ram’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "screen",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 8: The Voronoi Tessellation with the heat map overlaid for variable ’screen’ in the ’computers’ dataset


  hvtHmap(
  map_A,
  trainComputers,
  child.level = 1,
  hmap.cols = "ads",
  line.width = c(0.2),
  color.vec = c("#141B41"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 440,
)

Figure 9: The Voronoi Tessellation with the heat map overlaid for variable ’ads’ in the ’computers’ dataset

4 Map B : Compressed Novelty Map

Let us try to visualize the Map B from the flow diagram below.

Figure 10: Flow map with highlighted bounding box in red around map B

In this section, we will manually figure out the novelty cells from the plotted map A and store it in identified_Novelty_cells variable.

Note: For manual selecting the novelty cells from map A, one can enhance its interactivity by adding plotly elements to the code. This will transform map A into an interactive plot, allowing users to actively engage with the data. By hovering over the centroids of the cells, a tag containing segment child information will be displayed. Users can explore the map by hovering over different cells and selectively choose the novelty cells they wish to consider. Added an image for reference.

Figure 11: Manually selecting novelty cells

The removeNovelty function removes the identified novelty cell(s) from the training dataset (containing 5007 datapoints) and stores those records separately.

It takes input as the cell number (Segment.Child) of the manually identified novelty cell(s) and the compressed HVT map (map A) with 440 cells. It returns a list of two items: dataset with novelty records, and a subset of the dataset without the novelty records.

identified_Novelty_cells <<- c(73,321,332,338,435)   #73,321,332,338,435
output_list <- removeNovelty(identified_Novelty_cells, map_A)

[1] “The following cell(s) have been removed as novelties from the dataset: 73 321 332 338 435”

data_with_novelty <- output_list[[1]]
dataset_without_novelty <- output_list[[2]]

Let’s have a look at the data with novelties(containing 24 records). For the sake of brevity, we will only show the first 10 rows.

novelty_data <- data_with_novelty
novelty_data$Row.No <- row.names(novelty_data)
novelty_data <- novelty_data %>% dplyr::select("Row.No","Cell.ID","Cell.Number","price","speed","hd","ram","screen","ads")
colnames(novelty_data) <- c("Row.No","Cell.ID","Segment.Child","price","speed","hd","ram","screen","ads")
novelty_data %>% head(100) %>% 
  as.data.frame() %>%
  Table(scroll = T, limit = 20)

Row.No	Cell.ID	Segment.Child	price	speed	hd	ram	screen	ads
1	429	73	3.0762240	0.6794579	0.0421969	4.2031619	0.4307274	0.7120258
2	438	321	2.6449847	0.6794579	6.5710368	1.3676416	0.4307274	0.6851382
3	438	321	1.6578103	2.2969425	6.5710368	1.3676416	0.4307274	-1.2507722
4	439	332	0.9130997	0.6794579	4.6232922	2.7854017	0.4307274	-2.4607161
5	439	332	1.8569770	1.1076156	4.6232922	4.2031619	2.6404120	-2.4607161
6	439	332	1.5192595	2.2969425	4.6232922	4.2031619	0.4307274	-2.4607161
7	439	332	1.4482522	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161
8	439	332	1.8569770	1.1076156	4.6232922	4.2031619	2.6404120	-2.4607161
9	439	332	1.1555636	2.2969425	4.6232922	2.7854017	0.4307274	-2.4607161
10	439	332	1.7773103	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161
11	439	332	1.3460710	0.6794579	4.6232922	4.2031619	0.4307274	-2.4607161
12	439	332	1.4482522	1.1076156	4.6232922	4.2031619	0.4307274	-2.4607161
13	218	338	-1.4959522	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2993903
14	218	338	-1.5115391	0.6794579	0.4473277	-0.7589986	-0.6741149	-1.9767386
15	218	338	-1.1668940	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2859465
16	218	338	-1.4959522	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.4472723
17	218	338	-1.5981334	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2859465
18	218	338	-1.1668940	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.2993903
19	218	338	-1.1668940	0.6794579	0.4473277	-0.7589986	-0.6741149	-2.4472723
20	218	338	-1.3227637	0.6794579	0.4473277	-0.7589986	-0.6741149	-1.9767386

4.1 Voronoi Tessellation with highlighted novelty cell

The plotCells function is used to plot the Voronoi tessellation using the compressed HVT map (map A) containing 440 cells and highlights the identified novelty cell(s) i.e 5 cells (containing 24 records) in red on the map.

plotCells(identified_Novelty_cells, map_A,line.width = c(0.4),centroid.size = 0.01)

Figure 12: The Voronoi Tessellation constructed using the compressed HVT map (map A) with the novelty cell(s) highlighted in red

We pass the dataframe with novelty records (24 records) to HVT function along with other model parameters mentioned below to generate map B (layer2)

Model Parameters

Number of Cells at each Level = 12
Maximum Depth = 1
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

colnames(data_with_novelty) <- c("Cell.ID","Segment.Child","price","speed","hd","ram","screen","ads")
dataset_with_novelty <- data_with_novelty[,-1:-2]
map_B <- list()
mapA_scale_summary = map_A[[3]]$scale_summary
map_B <- muHVT::HVT(dataset_with_novelty,
                  n_cells = 12 ,   #6
                  depth = 1,
                  quant.err = 0.2,
                  projection.scale = 10,
                  normalize = F,
                  distance_metric = "L1_Norm",
                  error_metric = "max",
                  quant_method = "kmeans",
                  diagnose = F
                  )

The datatable displayed below is the summary from map B (layer 2) showing Cell.ID, Centroids and Quantization Error for each of the 12 cells.

summaryTable(map_B[[3]]$summary,scroll = T,limit = 500)

Segment.Level	Segment.Parent	Segment.Child	n	Cell.ID	Quant.Error	price	speed	hd	ram	screen	ads
1	1	1	1	6	0	-1.08	0.68	0.45	-0.76	-0.67	-1.98
1	1	2	1	3	0	-1.51	0.68	0.45	-0.76	-0.67	-1.98
1	1	3	2	1	0.01	-1.55	-0.08	0.45	-0.76	-0.67	-1.98
1	1	4	2	11	0	1.86	1.11	4.62	4.20	2.64	-2.46
1	1	5	1	7	0	3.08	0.68	0.04	4.20	0.43	0.71
1	1	6	5	8	0.31	1.39	0.94	4.62	3.92	0.43	-2.46
1	1	7	1	12	0	5.51	0.68	3.07	4.20	2.64	0.50
1	1	8	2	9	0.15	1.34	2.30	4.62	3.49	0.43	-2.46
1	1	9	1	4	0	-1.32	0.68	0.45	-0.76	-0.67	-1.98
1	1	10	3	2	0.02	-1.53	0.68	0.45	-0.76	-0.67	-2.34
1	1	11	2	10	0.38	2.15	1.49	6.57	1.37	0.43	-0.28
1	1	12	3	5	0.02	-1.17	0.68	0.45	-0.76	-0.67	-2.34

Now let’s check the compression summary for HVT (map B). The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

mapB_compression_summary <- map_B[[3]]$compression_summary %>%  dplyr::mutate_if(is.numeric, funs(round(.,4)))
compressionSummaryTable(mapB_compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	12	10	0.83	n_cells: 12 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 83% of the cells have hit the quantization threshold error.Since we are successfully able to attain the desired compression percentage, so we will not further subdivide the cells

5 Map C : Compressed Map without Novelty

Let us try to visualize the compressed Map C from the flow diagram below.

Figure 13:Flow map with highlighted bounding box in red around compressed map C

5.0.1 Iteration 1:

With the Novelties removed, we construct another hierarchical Voronoi tessellation map C layer 2 on the dataset without Novelty (containing 4986 records) and below mentioned model parameters.

Model Parameters

Number of Cells at each Level = 10
Maximum Depth = 2
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

map_C <- list()
mapA_scale_summary = map_A[[3]]$scale_summary
map_C <- muHVT::HVT(dataset_without_novelty,
                  n_cells = 10,
                  depth = 2,
                  quant.err = 0.2,
                  projection.scale = 10,
                  normalize = F,
                  distance_metric = "L1_Norm",
                  error_metric = "max",
                  quant_method = "kmeans",
                  diagnose = F,
                  scale_summary = mapA_scale_summary)

Now let’s check the compression summary for HVT (map C) where n_cell was set to 10. The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

mapC_compression_summary <- map_C[[3]]$compression_summary %>%  dplyr::mutate_if(is.numeric, funs(round(.,4)))
compressionSummaryTable(mapC_compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	10	0	0	n_cells: 10 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans
2	100	7	0.07	n_cells: 10 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 0% of the cells have hit the quantization threshold error in level 1 and 7% of the cells have hit the quantization threshold error in level 2

5.0.2 Iteration 2:

Since, we are yet to achive atleast 80% compression at depth 2. Let’s try to compress again using the below mentioned set of model parameters and the dataset without novelty (containing 4983 records).

Model Parameters

Number of Cells at each Level = 23
Maximum Depth = 2
Quantization Error Threshold = 0.2
Error Metric = Max
Distance Metric = Manhattan

map_C <- list()
map_C <- muHVT::HVT(dataset_without_novelty,
                  n_cells = 23,    #23
                  depth = 2,
                  quant.err = 0.2,
                  projection.scale = 10,
                  normalize = F,
                  distance_metric = "L1_Norm",
                  error_metric = "max",
                  quant_method = "kmeans",
                  diagnose = F,
                  scale_summary = mapA_scale_summary)

The datatable displayed below is the summary from map C (layer2). showing Cell.ID, Centroids and Quantization Error for each of the 531 cells.

summaryTable(map_C[[3]]$summary,scroll = T,limit = 500)

Segment.Level	Segment.Parent	Segment.Child	n	Cell.ID	Quant.Error	price	speed	hd	ram	screen	ads
1	1	1	391	512	0.56	-1.27	-1.04	-0.91	-0.83	-0.59	0.86
1	1	2	105	96	1.02	2.08	0.75	0.11	0.71	2.64	0.56
1	1	3	190	55	1.37	1.48	0.02	2.32	2.63	0.12	-0.22
1	1	4	86	15	0.94	0.87	1.27	2.61	2.57	1.33	-1.76
1	1	5	257	326	0.49	0.41	0.45	-0.08	-0.20	-0.67	0.66
1	1	6	116	201	0.77	1.57	0.45	-0.24	0.27	-0.30	-1.30
1	1	7	235	228	0.58	0.85	-0.82	0.42	1.37	-0.47	0.52
1	1	8	149	185	0.84	-0.79	1.73	0.76	-0.22	-0.28	-1.75
1	1	9	353	306	0.69	-0.77	0.55	0.28	-0.22	-0.17	-0.79
1	1	10	234	297	0.54	0.25	0.50	-0.11	-0.16	0.43	0.66
1	1	11	330	478	0.56	-1.18	-0.79	-0.70	-0.76	-0.52	-0.69
1	1	12	373	434	0.6	-0.50	0.40	-0.84	-0.84	-0.56	0.66
1	1	13	209	147	0.81	1.32	0.99	0.36	1.37	-0.01	0.70
1	1	14	337	471	0.36	-0.51	-0.97	-0.87	-0.75	-0.67	0.57
1	1	15	283	404	0.71	0.06	-0.40	-0.78	-0.40	-0.42	-1.42
1	1	16	168	142	0.85	0.13	0.66	0.48	-0.02	2.64	-0.97
1	1	17	285	384	0.53	-0.04	-0.96	0.05	-0.12	-0.65	0.54
1	1	18	118	301	0.75	0.35	2.30	-0.13	-0.29	-0.28	0.82
1	1	19	256	414	0.47	-0.47	-0.84	-0.43	-0.48	0.43	0.60
1	1	20	78	57	1	0.18	1.67	2.05	1.31	0.39	-1.97
1	1	21	171	360	0.64	0.24	-0.46	-0.37	-0.41	2.64	0.59
1	1	22	170	130	1.28	0.28	-0.01	1.64	0.99	0.21	-0.62
1	1	23	89	219	0.88	1.98	0.59	0.20	-0.20	0.12	0.58
2	1	1	19	528	0.09	-1.31	-1.17	-1.19	-1.11	-0.67	0.99
2	1	2	18	479	0.31	-1.42	-0.87	-0.21	-0.72	-0.67	0.53
2	1	3	28	521	0.1	-1.47	-1.22	-1.11	-0.76	-0.67	0.89
2	1	4	10	530	0.14	-1.84	-1.04	-1.20	-1.11	-0.67	0.79
2	1	5	27	519	0.1	-1.20	-1.09	-1.19	-1.11	-0.67	0.50
2	1	6	13	524	0.18	-1.36	-1.10	-0.99	-0.87	0.43	1.36
2	1	7	5	523	0.04	-1.19	-1.27	-0.78	-0.76	-0.67	1.57
2	1	8	23	494	0.06	-1.18	-0.89	-0.78	-0.76	-0.67	0.72
2	1	9	27	514	0.14	-1.35	-1.27	-1.08	-0.76	-0.67	0.39
2	1	10	35	510	0.1	-1.08	-1.27	-0.88	-0.76	-0.67	0.90
2	1	11	6	529	0.15	-1.22	-0.62	-1.19	-1.11	-0.67	1.50
2	1	12	10	513	0.07	-0.95	-0.89	-0.85	-0.76	-0.67	1.57
2	1	13	10	517	0.22	-1.70	-1.19	-0.73	-0.83	-0.67	0.57
2	1	14	11	472	0.17	-1.19	-1.13	-0.33	-0.05	-0.67	0.97
2	1	15	11	507	0.19	-1.00	-0.93	-1.17	-0.63	-0.67	0.97
2	1	16	7	531	0.07	-1.58	-1.27	-1.18	-0.96	-0.67	1.57
2	1	17	26	499	0.09	-1.31	-0.89	-0.92	-0.76	-0.67	0.36
2	1	18	23	515	0.07	-0.99	-0.89	-1.19	-1.11	-0.67	0.92
2	1	19	13	520	0.14	-1.56	-0.98	-0.74	-0.76	-0.67	1.34
2	1	20	15	489	0.12	-1.05	-0.89	-0.26	-0.76	-0.67	1.31
2	1	21	16	503	0.18	-1.48	-0.79	-0.73	-0.76	-0.67	0.76
2	1	22	15	518	0.26	-1.37	-1.07	-1.09	-1.07	0.43	0.66
2	1	23	23	500	0.07	-1.03	-0.89	-0.82	-0.76	-0.67	1.06
2	2	1	4	108	0.12	2.31	0.68	-0.12	-0.05	2.64	1.31
2	2	2	3	118	0.11	2.53	0.68	-0.47	-0.76	2.64	0.36
2	2	3	7	99	0.21	1.06	0.80	0.29	1.37	2.64	0.35
2	2	4	2	89	0.06	1.09	2.30	0.25	-0.05	2.64	1.57
2	2	5	7	59	0.13	3.21	0.68	-0.27	1.37	2.64	0.53
2	2	6	7	88	0.11	1.85	0.68	0.16	1.37	2.64	0.85
2	2	7	2	145	0.11	1.14	0.68	0.10	-0.05	2.64	1.36
2	2	8	3	38	0.11	3.32	0.68	-0.27	1.37	2.64	-0.78
2	2	9	6	100	0.17	2.86	0.68	0.10	-0.05	2.64	0.48
2	2	10	2	24	0.11	3.50	2.30	1.23	-0.05	2.64	-0.23
2	2	11	2	12	0.38	4.64	0.68	1.36	0.66	2.64	0.37
2	2	12	6	56	0.25	1.52	2.30	0.32	1.37	2.64	0.77
2	2	13	4	50	0.34	1.64	0.49	1.90	1.37	2.64	0.14
2	2	14	13	76	0.22	2.42	0.68	0.03	1.37	2.64	0.53
2	2	15	4	103	0.12	1.08	2.30	0.14	-0.05	2.64	0.52
2	2	16	11	167	0.13	1.23	0.68	-0.20	-0.05	2.64	0.59
2	2	17	1	107	0	1.99	-0.08	0.49	-0.05	2.64	-0.62
2	2	18	4	84	0.15	1.52	0.68	0.34	1.37	2.64	1.36
2	2	19	5	169	0.17	1.81	-0.08	-0.29	-0.19	2.64	0.99
2	2	20	4	70	0.11	2.69	-0.89	-0.27	1.37	2.64	0.69
2	2	21	4	122	0.11	1.99	0.68	0.22	-0.05	2.64	0.57
2	2	22	3	60	0.12	2.74	-0.89	-0.27	1.37	2.64	-0.78
2	2	23	1	195	0	1.63	0.68	-0.95	-0.76	2.64	0.69
2	3	1	5	39	0.04	1.54	-0.08	2.29	2.79	0.43	-0.81
2	3	2	3	9	0.05	1.71	-0.89	2.29	2.79	2.64	-0.55
2	3	3	10	51	0.07	1.10	-0.08	2.29	2.79	0.43	-0.56
2	3	4	5	32	0.39	3.74	-0.10	2.60	1.37	-0.67	0.96
2	3	5	3	19	0.02	2.16	2.30	2.29	2.79	-0.67	0.04
2	3	6	1	20	0	2.03	2.30	2.29	2.79	0.43	0.35
2	3	7	16	49	0.18	1.49	-0.89	2.29	2.79	0.43	-0.06
2	3	8	7	40	0.09	1.80	-0.08	2.29	2.79	0.43	-0.26
2	3	9	2	54	0.05	2.90	0.68	3.73	-0.05	-0.67	0.69
2	3	10	1	91	0	1.16	0.68	2.29	1.37	-0.67	0.04
2	3	11	7	46	0.05	1.16	0.68	2.29	2.79	0.43	-0.49
2	3	12	4	77	0.12	1.66	0.68	2.44	1.37	0.43	0.80
2	3	13	7	37	0.08	1.63	0.68	2.29	2.79	0.43	-0.73
2	3	14	12	53	0.1	1.69	0.68	2.29	2.79	-0.67	0.38
2	3	15	2	43	0.04	3.07	0.68	2.29	1.37	-0.67	-0.87
2	3	16	5	23	0.04	2.06	2.30	2.29	2.79	-0.67	0.35
2	3	17	12	45	0.06	0.92	0.68	2.29	2.79	0.43	-0.86
2	3	18	5	82	0.13	1.35	0.68	2.47	1.37	0.43	0.11
2	3	19	5	17	0.04	2.23	2.30	2.29	2.79	-0.67	0.69
2	3	20	24	61	0.12	1.30	-0.89	2.29	2.79	-0.67	0.33
2	3	21	9	36	0.18	2.07	0.68	2.29	2.79	0.43	-0.03
2	3	22	13	52	0.11	0.65	-0.08	2.29	2.79	0.43	-1.03
2	3	23	32	48	0.13	1.15	-0.89	2.29	2.79	0.43	-0.66
2	4	1	5	7	0.11	0.51	2.30	2.05	1.37	2.64	-2.33
2	4	2	8	18	0.07	0.52	0.68	3.07	2.79	0.43	-2.36
2	4	3	6	41	0.05	0.79	0.68	2.29	2.79	0.43	-1.24
2	4	4	2	3	0	1.08	2.30	3.07	2.79	0.43	-2.46
2	4	5	3	8	0.13	1.91	0.43	2.29	2.79	2.64	-0.55
2	4	6	1	11	0	0.01	1.11	3.07	2.79	0.43	-2.45
2	4	7	3	26	0.03	0.73	-0.08	3.07	2.79	0.43	-1.98
2	4	8	1	4	0	0.99	-0.08	3.07	2.79	2.64	-1.98
2	4	9	3	2	0.05	1.48	2.30	2.29	2.79	2.64	-1.08
2	4	10	2	14	0.02	0.81	0.68	3.07	2.79	0.43	-2.46
2	4	11	1	6	0	0.74	2.30	3.07	2.79	0.43	-1.98
2	4	12	2	16	0.02	0.89	1.11	3.07	2.79	0.43	-1.98
2	4	13	2	29	0.04	0.84	2.30	1.70	1.37	2.64	-1.02
2	4	14	6	5	0.03	0.74	2.30	3.07	2.79	0.43	-2.40
2	4	15	7	1	0.23	1.03	1.16	3.07	2.79	2.64	-2.34
2	4	16	5	25	0.02	1.02	2.30	2.29	2.79	0.43	-1.23
2	4	17	6	22	0.25	0.85	-0.22	2.29	2.79	2.64	-0.97
2	4	18	2	13	0.05	1.21	0.68	2.29	2.79	2.64	-1.10
2	4	19	5	30	0.11	0.32	0.68	2.17	1.37	2.64	-2.29
2	4	20	6	10	0.04	0.69	1.11	3.07	2.79	0.43	-2.32
2	4	21	1	34	0	0.30	-0.08	2.29	1.37	2.64	-1.98
2	4	22	6	21	0.06	1.46	2.30	2.29	2.79	0.43	-1.01
2	4	23	3	28	0.01	1.00	2.30	2.29	2.79	0.43	-0.79
2	5	1	24	300	0.13	0.57	0.68	0.13	-0.05	-0.67	0.72
2	5	2	9	332	0.08	-0.44	0.68	-0.04	-0.05	-0.67	0.21
2	5	3	5	310	0.12	0.37	-0.08	0.29	-0.05	-0.67	-0.14
2	5	4	21	312	0.17	0.35	0.86	0.11	-0.05	-0.67	1.39
2	5	5	6	311	0.06	-0.28	0.68	0.45	-0.05	-0.67	0.30
2	5	6	8	287	0.15	0.45	0.68	0.16	-0.05	-0.67	-0.20
2	5	7	5	305	0.06	-0.07	0.68	0.45	-0.05	-0.67	0.48
2	5	8	7	333	0.12	0.31	0.68	-0.57	-0.05	-0.67	0.57
2	5	9	12	319	0.06	-0.12	0.68	0.04	-0.05	-0.67	0.14
2	5	10	13	358	0.15	-0.04	-0.08	0.00	-0.05	-0.67	0.83
2	5	11	11	371	0.16	0.41	0.68	-0.40	-0.76	-0.67	0.94
2	5	12	7	314	0.16	1.02	0.68	-0.39	-0.15	-0.67	1.12
2	5	13	17	363	0.2	0.66	0.63	-0.69	-0.76	-0.67	0.15
2	5	14	15	278	0.11	1.06	0.68	0.06	-0.05	-0.67	0.76
2	5	15	8	356	0.16	0.13	0.79	-0.13	-0.76	-0.67	0.38
2	5	16	11	275	0.19	1.06	0.68	-0.09	-0.05	-0.67	0.19
2	5	17	23	334	0.13	0.51	-0.08	0.01	-0.05	-0.67	0.91
2	5	18	17	324	0.11	0.69	-0.08	-0.08	-0.05	-0.67	0.46
2	5	19	12	402	0.15	0.15	-0.08	-0.20	-0.76	-0.67	0.94
2	5	20	7	386	0.14	1.08	-0.08	-0.62	-0.76	-0.67	0.63
2	5	21	12	336	0.2	-0.17	0.68	-0.04	-0.05	-0.67	0.86
2	5	22	7	364	0.13	0.24	-0.08	-0.05	-0.05	-0.67	1.57
2	5	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	6	1	6	184	0.1	2.46	0.68	0.21	-0.05	-0.67	-0.87
2	6	2	9	133	0.29	1.43	0.51	-0.44	1.37	0.43	-1.15
2	6	3	2	233	0.08	1.08	0.68	0.47	-0.05	-0.67	-0.71
2	6	4	9	247	0.22	0.90	0.59	-0.69	-0.13	0.43	-1.39
2	6	5	2	194	0.08	2.30	0.30	-0.32	-0.05	-0.67	-1.71
2	6	6	8	238	0.26	1.04	0.58	-0.29	-0.14	0.43	-0.77
2	6	7	9	127	0.06	2.98	0.68	0.28	-0.05	-0.67	-1.67
2	6	8	4	165	0.11	1.26	-0.08	-0.07	1.37	-0.67	-1.37
2	6	9	5	124	0.19	1.76	0.53	-0.29	1.37	-0.67	-1.66
2	6	10	1	101	0	3.77	0.68	0.15	-0.05	-0.67	-1.72
2	6	11	5	292	0.22	1.44	0.53	-0.77	-0.33	-0.67	-1.67
2	6	12	1	365	0	1.35	-0.08	-0.67	-0.76	-0.67	-0.62
2	6	13	4	149	0.06	1.43	0.68	0.15	1.37	-0.67	-0.87
2	6	14	10	197	0.2	1.57	0.68	-0.47	-0.33	0.43	-1.64
2	6	15	3	159	0.2	1.48	2.30	0.28	-0.05	-0.67	-0.87
2	6	16	3	280	0.1	0.85	-0.08	0.22	-0.05	-0.67	-0.68
2	6	17	9	191	0.15	1.09	-0.93	-0.29	1.37	-0.67	-1.62
2	6	18	4	140	0.08	3.03	-0.08	0.15	-0.05	-0.67	-1.65
2	6	19	7	294	0.05	0.73	0.68	-0.69	-0.05	-0.67	-1.60
2	6	20	3	112	0.09	2.96	0.68	0.12	-0.05	0.43	-1.72
2	6	21	2	270	0.04	1.61	0.68	-0.71	-0.05	-0.67	-0.87
2	6	22	8	279	0.09	0.84	0.68	-0.36	-0.05	-0.67	-0.84
2	6	23	2	237	0.08	2.30	-0.49	0.15	-0.05	-0.67	-1.12
2	7	1	8	210	0.16	0.51	-1.18	0.46	1.37	0.43	1.10
2	7	2	16	231	0.12	1.01	-0.91	0.35	1.37	-0.67	0.40
2	7	3	12	211	0.1	1.39	-0.89	0.37	1.37	-0.67	0.29
2	7	4	8	209	0.06	0.35	-0.89	1.20	1.37	-0.67	0.35
2	7	5	5	263	0.05	0.58	-0.89	0.45	1.37	-0.67	1.57
2	7	6	12	202	0.18	0.49	-1.02	0.55	1.37	0.43	0.36
2	7	7	14	264	0.08	0.43	-0.89	0.45	1.37	-0.67	0.64
2	7	8	2	166	0.09	0.99	-0.08	0.45	1.37	0.43	0.26
2	7	9	11	216	0.14	0.82	-1.03	0.15	1.37	-0.67	-0.85
2	7	10	20	220	0.09	1.20	-0.91	0.45	1.37	-0.67	0.84
2	7	11	9	265	0.07	0.48	-1.27	0.45	1.37	-0.67	0.66
2	7	12	14	190	0.21	1.09	-0.89	0.28	1.37	0.43	0.63
2	7	13	9	222	0.09	0.82	-1.06	0.45	1.37	-0.67	-0.07
2	7	14	7	168	0.2	0.97	-1.00	0.03	1.37	0.43	-0.83
2	7	15	9	204	0.2	0.72	-0.08	0.45	1.37	-0.55	1.43
2	7	16	17	243	0.09	0.82	-0.98	0.46	1.37	-0.67	0.95
2	7	17	7	262	0.04	0.22	-0.89	0.45	1.37	-0.67	0.04
2	7	18	7	285	0.07	0.28	-1.22	0.45	1.37	-0.67	1.12
2	7	19	6	179	0.12	1.31	-0.08	0.35	1.37	-0.67	-0.26
2	7	20	17	189	0.14	1.26	-0.08	0.46	1.37	-0.67	0.84
2	7	21	3	316	0.02	0.10	-1.27	0.45	1.37	-0.67	1.57
2	7	22	11	183	0.11	1.44	-0.08	0.34	1.37	-0.67	0.42
2	7	23	11	251	0.09	0.72	-1.24	0.29	1.37	-0.67	0.36
2	8	1	5	116	0.08	-0.74	0.68	1.70	-0.05	0.43	-2.35
2	8	2	2	282	0	-1.43	0.68	0.45	-0.76	0.43	-2.29
2	8	3	8	182	0.09	-0.47	2.30	0.49	-0.05	0.43	-0.85
2	8	4	10	236	0.19	-0.61	2.30	0.49	-0.05	-0.67	-1.01
2	8	5	13	87	0.15	-0.66	2.30	1.70	-0.05	-0.16	-2.28
2	8	6	4	117	0.13	-1.29	0.49	2.29	-0.76	-0.67	-2.17
2	8	7	2	64	0.02	-1.12	2.30	2.29	-0.76	-0.67	-2.37
2	8	8	3	104	0.06	-0.17	2.30	2.29	-0.05	-0.67	-0.98
2	8	9	4	170	0.08	-0.90	1.11	1.35	-0.05	0.43	-1.21
2	8	10	7	146	0.07	-0.91	2.30	0.47	-0.05	-0.67	-2.34
2	8	11	12	409	0.11	-0.78	2.30	-0.33	-0.76	-0.67	-1.03
2	8	12	7	241	0.1	-0.19	2.30	0.33	-0.05	-0.67	-0.89
2	8	13	4	258	0.09	0.04	2.30	-0.08	-0.05	-0.67	-1.04
2	8	14	9	330	0.09	-1.17	2.30	0.45	-0.76	-0.67	-2.26
2	8	15	6	212	0.13	-0.74	0.68	0.30	-0.05	0.43	-2.40
2	8	16	7	175	0.07	-0.53	2.30	0.49	-0.05	0.43	-1.23
2	8	17	4	109	0.04	-0.38	2.30	1.70	-0.05	0.43	-1.05
2	8	18	10	144	0.09	-0.89	0.68	1.70	-0.05	-0.67	-2.20
2	8	19	1	283	0	-1.59	0.68	-0.19	-0.05	0.43	-2.29
2	8	20	7	123	0.17	-0.94	2.30	0.46	-0.35	0.43	-2.27
2	8	21	3	329	0.06	-0.86	2.30	-0.29	-0.76	0.43	-0.98
2	8	22	21	277	0.11	-1.16	0.68	0.48	-0.05	-0.67	-2.25
2	8	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	9	1	33	331	0.22	-0.57	-0.08	0.41	-0.05	-0.67	-0.71
2	9	2	10	368	0.19	-0.75	0.68	-0.60	-0.76	0.43	-0.42
2	9	3	13	423	0.19	-1.02	0.68	-0.69	-0.70	-0.67	-0.83
2	9	4	8	335	0.2	-0.08	0.68	-0.49	-0.23	-0.67	-0.86
2	9	5	15	377	0.21	-1.31	-0.08	0.30	-0.76	0.43	-0.98
2	9	6	8	224	0.04	-0.74	1.11	0.50	-0.05	0.43	-1.22
2	9	7	22	261	0.13	-0.22	0.68	0.29	-0.05	0.43	-0.60
2	9	8	14	381	0.28	-0.89	0.57	-0.09	-0.51	-0.67	-0.23
2	9	9	14	257	0.08	-0.64	0.68	0.49	-0.05	0.43	-0.76
2	9	10	13	354	0.27	-1.37	0.33	0.47	-0.27	-0.67	-0.93
2	9	11	9	248	0.16	-1.00	0.68	0.57	-0.05	0.43	-1.22
2	9	12	23	323	0.13	-0.51	0.68	0.02	-0.05	-0.67	-0.46
2	9	13	10	344	0.25	-1.39	0.68	-0.30	-0.48	0.43	-1.23
2	9	14	11	267	0.11	-1.03	0.68	0.41	-0.05	0.43	-0.85
2	9	15	20	422	0.17	-1.43	0.68	-0.23	-0.72	-0.67	-1.02
2	9	16	23	206	0.26	-0.52	0.70	1.25	-0.05	0.43	-0.79
2	9	17	8	304	0.13	-0.86	0.68	-0.12	-0.05	0.43	-0.39
2	9	18	8	240	0.05	-0.72	1.11	0.50	-0.05	0.43	-0.83
2	9	19	32	303	0.35	-0.82	0.68	0.33	0.04	-0.67	-1.07
2	9	20	13	317	0.16	-1.11	0.68	0.34	-0.76	0.43	-0.90
2	9	21	10	308	0.22	-0.75	-0.24	0.33	-0.05	0.43	-0.69
2	9	22	33	296	0.2	-0.39	0.68	0.55	-0.05	-0.67	-0.56
2	9	23	3	196	0.03	-0.83	-0.08	1.70	-0.05	-0.67	-1.98
2	10	1	4	269	0.07	0.51	0.68	-0.55	-0.05	0.43	-0.62
2	10	2	8	327	0.1	-0.43	0.68	-0.12	-0.05	0.43	1.46
2	10	3	23	318	0.15	0.23	-0.08	-0.23	-0.05	0.43	0.69
2	10	4	3	346	0.12	-0.04	0.68	-0.74	-0.76	0.43	-0.31
2	10	5	9	271	0.17	0.14	0.73	0.34	-0.05	0.43	1.57
2	10	6	10	273	0.12	0.92	0.68	-0.54	-0.05	0.43	0.74
2	10	7	9	299	0.11	0.93	-0.08	-0.36	-0.05	0.43	0.66
2	10	8	6	307	0.08	-0.01	0.68	-0.52	-0.05	0.43	0.38
2	10	9	8	289	0.15	0.67	-0.08	-0.27	-0.05	0.43	-0.15
2	10	10	13	325	0.26	0.16	-0.08	0.00	-0.16	0.43	1.23
2	10	11	13	315	0.18	-0.46	0.68	-0.14	-0.05	0.43	0.64
2	10	12	10	259	0.12	0.83	0.68	-0.14	-0.05	0.43	0.32
2	10	13	7	234	0.06	-0.20	0.68	1.23	-0.05	0.43	0.35
2	10	14	12	260	0.16	0.74	0.72	0.08	-0.05	0.43	0.89
2	10	15	8	249	0.12	0.66	0.68	0.48	-0.14	0.43	0.81
2	10	16	13	268	0.09	0.04	0.68	0.49	-0.05	0.43	0.60
2	10	17	11	256	0.11	0.31	0.68	0.24	-0.05	0.43	-0.06
2	10	18	6	342	0.17	0.82	0.30	-0.76	-0.76	0.43	0.64
2	10	19	8	302	0.15	0.22	0.68	-0.33	-0.05	0.43	1.20
2	10	20	12	370	0.24	-0.28	0.75	-0.51	-0.76	0.43	0.84
2	10	21	19	281	0.11	0.29	0.68	-0.07	-0.05	0.43	0.64
2	10	22	9	290	0.13	-0.34	0.68	0.15	-0.05	0.43	0.20
2	10	23	13	351	0.11	0.21	0.68	-0.70	-0.76	0.43	0.66
2	11	1	10	411	0.22	-1.09	-0.93	0.23	-0.12	-0.67	-0.72
2	11	2	14	502	0.14	-1.13	-0.97	-1.01	-0.86	-0.67	-0.09
2	11	3	7	481	0.2	-0.81	-0.08	-1.20	-1.11	-0.36	-0.96
2	11	4	19	516	0.16	-0.95	-0.95	-1.13	-0.91	-0.67	-1.64
2	11	5	14	482	0.12	-1.58	-0.89	-0.16	-0.76	-0.67	-0.68
2	11	6	24	498	0.16	-1.36	-0.94	-0.85	-0.77	-0.67	-0.46
2	11	7	17	527	0.25	-1.29	-1.25	-1.06	-0.84	-0.61	-1.65
2	11	8	23	484	0.15	-1.01	-0.92	-0.84	-0.79	-0.67	-0.72
2	11	9	18	509	0.1	-1.67	-0.91	-0.78	-0.76	-0.67	-0.75
2	11	10	10	435	0.15	-0.94	-0.08	-0.59	-0.69	-0.67	-0.89
2	11	11	19	491	0.25	-1.54	-0.93	-0.50	-0.74	-0.67	-0.11
2	11	12	12	452	0.25	-1.47	-0.76	-0.12	-0.79	0.43	-0.55
2	11	13	8	445	0.17	-1.22	-0.08	-0.43	-0.67	-0.67	-0.19
2	11	14	8	427	0.08	-0.60	-0.89	-0.75	-0.05	-0.67	-0.70
2	11	15	17	466	0.16	-0.52	-0.98	-0.84	-0.76	-0.67	-0.67
2	11	16	17	511	0.16	-1.09	-1.00	-1.06	-0.88	-0.67	-1.12
2	11	17	8	437	0.08	-0.69	-0.08	-0.75	-0.76	-0.67	-0.48
2	11	18	9	465	0.09	-1.33	-0.08	-0.78	-0.76	-0.67	-0.56
2	11	19	10	525	0.18	-1.69	-1.19	-1.13	-0.90	-0.67	-0.23
2	11	20	30	473	0.29	-1.06	-0.87	-0.89	-0.79	0.43	-0.61
2	11	21	6	450	0.1	-1.62	-0.08	-0.06	-0.76	-0.67	-0.70
2	11	22	18	457	0.26	-1.05	-0.89	-0.10	-0.68	-0.67	-0.16
2	11	23	12	453	0.09	-1.43	-0.08	-0.29	-0.76	-0.67	-1.02
2	12	1	25	397	0.2	-0.09	0.68	-0.76	-0.70	-0.67	0.48
2	12	2	13	394	0.18	-0.08	0.68	-0.81	-0.79	-0.67	-0.16
2	12	3	13	413	0.17	0.17	0.68	-1.08	-0.87	-0.67	0.72
2	12	4	21	474	0.12	-0.56	0.68	-1.19	-1.10	-0.67	0.96
2	12	5	15	442	0.11	-0.32	0.68	-1.19	-1.09	-0.67	0.31
2	12	6	12	458	0.26	-0.88	0.68	-0.73	-0.70	-0.67	1.24
2	12	7	17	405	0.25	-0.57	0.68	-0.86	-0.90	0.43	0.44
2	12	8	23	421	0.14	0.08	-0.08	-0.77	-0.76	-0.67	0.62
2	12	9	24	468	0.16	-0.66	-0.08	-1.13	-0.98	-0.67	0.37
2	12	10	6	433	0.13	-0.26	-0.08	-0.91	-0.88	-0.67	-0.11
2	12	11	8	449	0.09	-0.21	0.84	-0.78	-0.76	-0.67	1.57
2	12	12	10	454	0.22	-0.75	0.72	-0.93	-0.94	0.43	1.25
2	12	13	19	428	0.17	-0.83	-0.08	-0.26	-0.68	-0.67	0.55
2	12	14	8	506	0.12	-0.51	1.05	-1.19	-1.11	-0.67	1.36
2	12	15	9	443	0.16	-0.77	-0.08	-0.08	-0.76	-0.67	1.22
2	12	16	21	441	0.19	-0.46	-0.08	-0.80	-0.62	-0.67	0.85
2	12	17	21	436	0.14	-0.85	0.68	-0.87	-0.76	-0.67	0.44
2	12	18	27	492	0.15	-0.84	-0.08	-1.17	-1.05	-0.67	0.90
2	12	19	9	495	0.15	-0.81	-0.08	-0.91	-0.88	-0.67	1.48
2	12	20	19	431	0.24	-0.99	0.68	-0.80	-0.78	-0.67	-0.34
2	12	21	16	387	0.2	-0.62	0.68	-0.11	-0.67	-0.67	0.49
2	12	22	10	477	0.19	-0.79	-0.08	-1.19	-1.11	0.43	0.74
2	12	23	27	415	0.13	-0.11	0.76	-0.76	-0.76	-0.67	1.01
2	13	1	10	138	0.21	0.94	0.68	0.67	1.37	0.43	-0.06
2	13	2	14	131	0.21	2.10	0.68	0.25	1.37	0.43	0.77
2	13	3	4	198	0.02	0.61	0.68	0.45	1.37	-0.67	0.69
2	13	4	2	47	0.19	1.96	1.49	2.29	1.37	0.43	1.36
2	13	5	8	143	0.08	1.28	1.11	0.45	1.37	-0.67	1.36
2	13	6	4	98	0.2	0.98	2.30	0.73	1.37	0.43	0.00
2	13	7	10	155	0.13	1.55	0.68	-0.28	1.37	0.43	0.33
2	13	8	24	151	0.12	1.45	0.68	0.22	1.37	0.43	0.72
2	13	9	5	164	0.11	1.30	-0.08	0.40	1.37	0.43	0.73
2	13	10	12	176	0.12	1.08	0.68	-0.15	1.37	0.43	0.72
2	13	11	10	94	0.13	1.39	2.30	0.44	1.37	0.43	1.23
2	13	12	12	154	0.12	1.75	0.68	0.36	1.37	-0.67	0.29
2	13	13	4	174	0.1	0.56	0.68	0.82	1.37	-0.67	0.19
2	13	14	18	153	0.27	0.89	0.75	0.30	1.37	0.43	1.33
2	13	15	10	172	0.22	0.58	0.77	0.34	1.37	0.43	0.47
2	13	16	8	177	0.08	0.88	0.68	0.45	1.37	-0.67	1.41
2	13	17	2	125	0.09	2.71	0.68	0.34	1.37	-0.67	0.67
2	13	18	8	95	0.07	1.62	2.30	0.45	1.37	-0.67	1.41
2	13	19	7	173	0.07	1.27	0.68	0.45	1.37	-0.67	0.74
2	13	20	7	128	0.1	1.78	0.68	0.38	1.37	0.43	-0.02
2	13	21	16	111	0.2	1.15	2.30	0.59	1.37	-0.67	0.18
2	13	22	10	152	0.11	1.78	0.68	0.46	1.37	-0.67	0.90
2	13	23	4	162	0.07	1.30	0.68	0.46	1.37	-0.67	0.05
2	14	1	46	475	0.08	-0.74	-0.89	-0.78	-0.76	-0.67	0.52
2	14	2	36	469	0.16	-0.24	-0.94	-0.83	-0.76	-0.67	0.94
2	14	3	19	439	0.18	-0.52	-0.95	-0.74	-0.05	-0.67	0.60
2	14	4	13	459	0.15	0.21	-1.04	-0.93	-0.76	-0.67	0.60
2	14	5	24	501	0.14	-0.59	-1.00	-1.16	-0.89	-0.67	0.87
2	14	6	44	480	0.13	-0.63	-0.93	-0.78	-0.76	-0.67	0.98
2	14	7	35	493	0.13	-0.68	-0.91	-1.17	-0.98	-0.67	0.42
2	14	8	19	451	0.1	-0.20	-0.95	-0.74	-0.76	-0.67	-0.06
2	14	9	19	483	0.14	-0.72	-1.03	-0.93	-0.85	-0.67	-0.07
2	14	10	34	488	0.13	-0.61	-1.27	-0.93	-0.76	-0.67	0.54
2	14	11	11	455	0.18	-0.85	-0.89	-0.17	-0.69	-0.67	0.45
2	14	12	37	462	0.1	-0.26	-0.91	-0.85	-0.76	-0.67	0.39
2	14	13	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	14	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	15	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	16	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	17	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	18	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	19	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	20	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	21	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	22	0	NA	NA	NA	NA	NA	NA	NA	NA
2	14	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	15	1	13	341	0.17	0.19	-0.92	-0.52	-0.05	0.43	-0.93
2	15	2	16	361	0.16	0.24	0.68	-0.91	-0.45	-0.67	-1.62
2	15	3	5	456	0.11	-0.29	-0.89	-0.70	-0.76	-0.67	-1.12
2	15	4	26	461	0.2	-0.35	-0.08	-1.04	-0.81	-0.67	-1.59
2	15	5	16	444	0.3	-0.40	0.68	-1.15	-0.94	-0.61	-1.48
2	15	6	15	486	0.08	-0.50	-0.89	-0.92	-0.76	-0.67	-1.66
2	15	7	5	203	0.2	0.69	-1.12	-0.43	1.37	-0.23	-1.58
2	15	8	9	426	0.33	-0.40	-0.75	-0.77	-0.68	0.43	-1.23
2	15	9	14	369	0.19	0.28	-0.92	-0.38	-0.05	-0.67	-0.90
2	15	10	11	464	0.07	-0.06	-0.89	-0.82	-0.76	-0.67	-1.66
2	15	11	5	460	0.11	0.54	-0.89	-0.88	-0.76	-0.67	-1.70
2	15	12	7	410	0.17	0.51	-0.08	-0.92	-0.76	-0.67	-1.57
2	15	13	17	352	0.15	0.35	-0.89	-0.72	-0.13	0.43	-1.66
2	15	14	11	420	0.08	0.02	-1.03	-0.67	-0.05	-0.67	-1.65
2	15	15	13	396	0.13	0.43	-0.89	-0.71	-0.05	-0.67	-1.63
2	15	16	4	379	0.23	0.75	-0.69	-0.65	-0.76	0.15	-0.67
2	15	17	7	322	0.21	0.47	0.24	-0.72	-0.66	0.43	-1.52
2	15	18	25	343	0.22	0.36	-0.08	-0.63	-0.05	-0.67	-1.53
2	15	19	15	496	0.09	-0.38	-1.04	-1.11	-0.76	-0.67	-1.63
2	15	20	14	380	0.3	0.03	-0.08	-0.55	-0.46	-0.67	-0.82
2	15	21	14	293	0.21	0.50	-0.08	-0.67	-0.05	0.43	-1.14
2	15	22	10	353	0.17	0.20	0.68	-0.81	-0.48	-0.67	-0.92
2	15	23	11	432	0.09	-0.35	-0.89	-0.80	-0.05	-0.67	-1.61
2	16	1	8	58	0.25	0.04	2.30	1.70	-0.05	2.64	-1.20
2	16	2	1	33	0	1.25	2.30	1.20	1.37	2.64	-0.94
2	16	3	13	90	0.39	0.07	2.30	0.48	-0.05	2.64	-0.99
2	16	4	5	230	0.11	0.87	-0.89	-0.67	-0.33	2.64	-1.64
2	16	5	11	163	0.15	-0.13	0.68	0.38	-0.05	2.64	-0.78
2	16	6	4	75	0.04	0.84	-0.89	1.20	1.37	2.64	-0.83
2	16	7	17	207	0.2	-0.62	0.68	0.36	-0.76	2.64	-1.01
2	16	8	20	135	0.14	-0.24	0.98	0.51	-0.05	2.64	-1.10
2	16	9	9	132	0.15	1.33	-0.08	-0.65	-0.29	2.64	-1.52
2	16	10	8	74	0.2	0.95	0.39	1.10	1.37	2.64	-0.65
2	16	11	8	284	0.05	-0.76	-0.08	0.51	-0.76	2.64	-1.16
2	16	12	5	83	0.04	0.78	-0.89	1.20	1.37	2.64	-0.43
2	16	13	5	105	0.07	-0.44	0.68	0.49	-0.05	2.64	-2.23
2	16	14	4	136	0.09	1.24	0.68	-0.64	-0.05	2.64	-0.87
2	16	15	2	106	0.03	1.56	0.68	-0.71	-0.76	2.64	-1.71
2	16	16	9	120	0.06	-0.01	0.68	1.23	-0.05	2.64	-0.72
2	16	17	7	67	0.21	0.47	0.41	1.49	1.37	2.64	-0.97
2	16	18	7	213	0.33	0.07	0.57	-0.21	-0.05	2.64	-0.36
2	16	19	5	160	0.15	0.43	0.68	0.62	-0.05	2.64	0.17
2	16	20	6	320	0.13	-0.58	0.68	-0.26	-0.76	2.64	-0.44
2	16	21	3	85	0.06	0.38	0.68	2.29	-0.05	2.64	-0.61
2	16	22	6	345	0.1	-0.82	-0.08	0.21	-0.76	2.64	-0.79
2	16	23	5	110	0.06	1.39	0.68	-0.67	-0.05	2.64	-1.61
2	17	1	6	440	0.09	-0.58	-1.27	0.12	-0.05	-0.67	1.29
2	17	2	10	416	0.13	-0.58	-0.93	-0.18	-0.05	-0.67	0.64
2	17	3	12	417	0.09	-0.24	-0.92	0.15	-0.05	-0.67	1.57
2	17	4	9	349	0.12	0.29	-0.93	0.23	-0.05	-0.67	-0.11
2	17	5	5	309	0.28	0.52	-0.89	0.35	-0.05	0.43	0.52
2	17	6	16	385	0.13	0.31	-0.89	-0.45	-0.05	-0.67	0.41
2	17	7	23	372	0.09	0.31	-0.91	0.03	-0.05	-0.67	0.86
2	17	8	21	403	0.09	-0.15	-1.27	0.09	-0.05	-0.67	0.85
2	17	9	14	366	0.13	-0.37	-0.92	0.45	-0.05	-0.67	-0.31
2	17	10	11	419	0.13	-0.26	-0.89	-0.36	-0.05	-0.67	1.02
2	17	11	13	389	0.11	-0.35	-0.95	0.02	-0.05	-0.67	0.00
2	17	12	17	392	0.06	-0.26	-0.89	0.05	-0.05	-0.67	0.62
2	17	13	4	362	0.17	0.15	-1.08	0.01	-0.05	-0.67	-0.67
2	17	14	9	424	0.17	0.37	-0.89	-0.29	-0.76	-0.67	0.90
2	17	15	12	395	0.09	-0.06	-1.27	-0.09	-0.05	-0.67	0.37
2	17	16	18	399	0.11	-0.17	-0.89	0.09	-0.05	-0.67	1.05
2	17	17	15	373	0.09	-0.59	-0.89	0.45	-0.05	-0.67	-0.58
2	17	18	11	357	0.09	0.20	-0.89	0.47	-0.05	-0.67	0.68
2	17	19	15	398	0.26	0.98	-0.89	-0.41	-0.57	-0.67	0.49
2	17	20	20	367	0.08	0.25	-0.91	0.05	-0.05	-0.67	0.46
2	17	21	3	438	0.08	-0.60	-0.89	0.04	-0.76	-0.67	0.48
2	17	22	6	429	0.08	-0.22	-0.89	-0.07	-0.76	-0.67	0.68
2	17	23	15	382	0.08	-0.35	-0.92	0.45	-0.05	-0.67	0.47
2	18	1	4	430	0.06	-0.12	2.30	-0.78	-0.76	-0.67	0.52
2	18	2	5	199	0.07	0.53	2.30	0.46	-0.05	0.43	0.62
2	18	3	4	276	0.04	-0.21	2.30	0.03	-0.05	-0.67	-0.48
2	18	4	1	225	0	0.74	2.30	-0.19	-0.05	-0.67	-0.79
2	18	5	4	340	0.08	0.42	2.30	-0.56	-0.76	-0.67	-0.64
2	18	6	16	286	0.15	0.56	2.30	0.00	-0.05	-0.67	1.49
2	18	7	5	242	0.04	0.41	2.30	0.45	-0.05	-0.67	0.35
2	18	8	7	187	0.09	0.61	2.30	0.31	-0.05	0.43	1.57
2	18	9	8	200	0.15	0.62	2.30	0.08	-0.05	0.43	0.04
2	18	10	5	226	0.04	0.41	2.30	0.05	-0.05	0.43	0.69
2	18	11	8	463	0.07	0.24	2.30	-0.78	-0.76	-0.67	1.30
2	18	12	9	522	0.08	-0.14	2.30	-1.19	-1.11	-0.67	1.33
2	18	13	2	115	0.08	1.74	2.30	1.23	-0.05	-0.67	-0.03
2	18	14	6	274	0.18	0.31	2.30	-0.06	-0.05	-0.67	0.15
2	18	15	3	229	0.06	0.55	2.30	-0.06	-0.05	0.43	1.14
2	18	16	10	254	0.13	0.67	2.30	0.22	-0.05	-0.67	0.87
2	18	17	4	407	0.13	0.00	2.30	-0.78	-0.76	0.43	0.94
2	18	18	7	246	0.08	0.16	2.30	-0.04	-0.05	0.43	1.57
2	18	19	4	339	0.04	-0.04	2.30	0.05	-0.76	-0.67	0.04
2	18	20	3	217	0.01	0.65	2.30	0.03	-0.05	0.43	0.69
2	18	21	3	476	0.14	-0.52	2.30	-0.45	-0.76	-0.67	1.29
2	18	22	0	NA	NA	NA	NA	NA	NA	NA	NA
2	18	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	19	1	6	391	0.18	0.52	-0.89	-0.34	-0.64	0.43	0.98
2	19	2	6	383	0.07	0.68	-0.89	-0.56	-0.76	0.43	0.10
2	19	3	21	470	0.12	-1.02	-0.91	-0.76	-0.76	0.43	0.53
2	19	4	11	348	0.2	0.08	-1.03	-0.10	-0.05	0.43	0.14
2	19	5	10	446	0.15	-1.12	-0.81	-0.30	-0.76	0.43	0.01
2	19	6	14	350	0.08	0.07	-0.89	0.05	-0.05	0.43	0.74
2	19	7	8	388	0.27	-0.53	-0.38	-0.56	-0.40	0.43	-0.28
2	19	8	7	467	0.11	-1.18	-0.89	-0.29	-0.76	0.43	0.88
2	19	9	6	425	0.09	-0.49	-0.89	-0.23	-0.76	0.43	0.58
2	19	10	9	406	0.1	-0.18	-0.08	-0.75	-0.76	0.43	0.63
2	19	11	10	400	0.16	-0.36	-0.89	-0.71	-0.05	0.43	0.33
2	19	12	9	347	0.21	-0.22	-0.89	0.49	-0.13	0.43	0.63
2	19	13	8	497	0.1	-0.83	-0.89	-1.19	-1.11	0.43	0.44
2	19	14	16	378	0.12	-0.25	-1.03	-0.12	-0.05	0.43	0.74
2	19	15	16	418	0.15	-0.73	-0.08	-0.41	-0.76	0.43	0.82
2	19	16	22	448	0.15	-0.21	-0.94	-0.82	-0.76	0.43	0.77
2	19	17	13	485	0.15	-0.74	-0.95	-0.83	-0.76	0.43	1.20
2	19	18	13	447	0.14	-0.52	-0.98	-0.75	-0.76	0.43	0.17
2	19	19	10	412	0.21	-0.48	-0.81	-0.43	-0.05	0.43	1.25
2	19	20	11	355	0.16	-0.61	-0.89	0.20	-0.05	0.43	-0.14
2	19	21	9	390	0.1	-0.66	-0.89	-0.14	-0.05	0.43	0.65
2	19	22	10	401	0.15	-0.52	-0.93	0.13	-0.05	0.43	1.40
2	19	23	11	487	0.12	-0.83	-1.27	-0.86	-0.76	0.43	0.75
2	20	1	5	73	0.02	-0.13	0.68	1.70	1.37	0.43	-2.46
2	20	2	3	69	0.02	-0.02	0.68	2.29	1.37	0.43	-1.98
2	20	3	3	65	0.19	-0.83	1.22	3.07	-0.05	-0.67	-2.46
2	20	4	7	79	0.03	0.82	2.30	1.21	1.37	0.43	-0.92
2	20	5	5	62	0.03	0.13	0.68	2.29	1.37	0.43	-2.32
2	20	6	3	80	0.04	-0.03	0.68	1.70	1.37	0.43	-2.19
2	20	7	3	71	0.03	0.47	2.30	1.70	1.37	0.43	-0.79
2	20	8	2	72	0	0.22	0.68	1.70	1.37	0.43	-2.46
2	20	9	12	35	0.14	0.15	2.30	2.29	1.37	0.43	-2.29
2	20	10	4	44	0.06	-0.03	0.68	3.07	1.37	0.43	-2.25
2	20	11	11	42	0.07	0.15	2.30	1.70	1.37	0.43	-2.39
2	20	12	1	31	0	0.57	2.30	3.30	1.37	0.43	-1.25
2	20	13	5	63	0.08	-0.29	0.68	2.29	1.37	0.43	-2.35
2	20	14	6	66	0.05	0.39	2.30	1.70	1.37	0.43	-1.23
2	20	15	1	68	0	0.31	0.68	2.29	1.37	0.43	-1.98
2	20	16	3	78	0.05	1.06	2.30	1.23	1.37	0.43	-0.62
2	20	17	4	27	0.05	0.19	2.30	3.07	1.37	0.43	-2.25
2	20	18	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	19	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	20	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	21	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	22	0	NA	NA	NA	NA	NA	NA	NA	NA
2	20	23	0	NA	NA	NA	NA	NA	NA	NA	NA
2	21	1	4	504	0.17	0.72	-0.89	-0.66	-0.76	2.64	1.36
2	21	2	5	245	0.17	0.97	0.68	-0.65	-0.62	2.64	1.03
2	21	3	6	313	0.16	-0.20	0.68	-0.40	-0.52	2.64	0.41
2	21	4	10	338	0.18	0.27	-0.89	-0.34	-0.05	2.64	0.32
2	21	5	4	375	0.13	0.16	-0.49	0.04	-0.76	2.64	0.66
2	21	6	6	298	0.15	1.35	-0.08	-0.45	-0.76	2.64	0.94
2	21	7	10	526	0.21	-0.31	-1.08	-0.88	-0.76	2.64	1.28
2	21	8	11	490	0.15	-0.83	-0.89	-0.25	-0.76	2.64	-0.33
2	21	9	6	393	0.19	1.39	-0.89	-0.56	-0.76	2.64	-0.04
2	21	10	8	359	0.11	0.49	-0.89	-0.39	-0.05	2.64	0.95
2	21	11	6	295	0.1	0.19	-0.08	-0.29	-0.05	2.64	0.75
2	21	12	7	376	0.09	-0.05	-0.89	-0.19	-0.05	2.64	0.77
2	21	13	18	205	0.2	0.58	0.68	-0.13	-0.13	2.64	0.65
2	21	14	6	186	0.18	0.55	0.55	0.01	-0.05	2.64	1.50
2	21	15	6	328	0.14	0.27	-0.89	-0.73	-0.05	2.64	-0.76
2	21	16	2	215	0.1	0.75	-0.08	-0.46	-0.05	2.64	-0.35
2	21	17	5	408	0.12	-0.53	-0.08	-0.29	-0.76	2.64	0.19

Now let’s check the compression summary for HVT (map C). The table below shows no of cells, no of cells having quantization error below threshold and percentage of cells having quantization error below threshold for each level.

mapC_compression_summary <- map_C[[3]]$compression_summary %>%  dplyr::mutate_if(is.numeric, funs(round(.,4)))
compressionSummaryTable(mapC_compression_summary)

segmentLevel	noOfCells	noOfCellsBelowQuantizationError	percentOfCellsBelowQuantizationErrorThreshold	parameters
1	23	0	0	n_cells: 23 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans
2	508	434	0.85	n_cells: 23 quant.err: 0.2 distance_metric: L1_Norm error_metric: max quant_method: kmeans

As it can be seen from the table above, 0% of the cells have hit the quantization threshold error in level 1 and 85% of the cells have hit the quantization threshold error in level 2

Let’s plot the Voronoi tessellation for layer 2 (map C)

muHVT::plotHVT(map_C,
        line.width = c(0.4,0.2), 
        color.vec = c("#141B41","#0582CA"),
        centroid.size = 0.1,
        maxDepth = 2)

Figure 14: The Voronoi Tessellation for layer 2 (map C) shown for the 100 cells in the dataset ’computers’ at level 2

Heat Maps

Now let’s plot all the features for each cell at level two as a heatmap for better visualization.


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "n",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 15: The Voronoi Tessellation with the heat map overlaid for features No. of entities in each cell


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "price",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 16: The Voronoi Tessellation with the heat map overlaid for features price in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "speed",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 17: The Voronoi Tessellation with the heat map overlaid for features speed in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "hd",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 18: The Voronoi Tessellation with the heat map overlaid for features hd in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "ram",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 19: The Voronoi Tessellation with the heat map overlaid for features ram in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "screen",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 20: The Voronoi Tessellation with the heat map overlaid for features screen in the ’computers’ dataset


  hvtHmap(
  map_C,
  trainComputers,
  child.level = 2,
  hmap.cols = "ads",
  line.width = c(0.6,0.4),
  color.vec = c("#141B41","#0582CA"),
  palette.color = 6,
  centroid.size = 0.1,
  show.points = T,
  quant.error.hmap = 0.2,
  n_cells.hmap = 100,
)

Figure 21: The Voronoi Tessellation with the heat map overlaid for features ads in the ’computers’ dataset

We now have the set of maps (map A, map B & map C) which will be used to predict which map and cell each test record is assigned to, but before that lets view our test dataset

6 Prediction on Test Data

Now once we have built the model, let us try to predict using our test dataset (containing 1252 data points) which cell and which layer each point belongs to.

Raw Testing Dataset

The testing dataset includes the following columns:

Price: The price of 486 PCs in US dollars.
Speed: The clock speed of the PCs in MHz.
HD: The size of the hard drive in MB.
RAM: The size of RAM in MB.
Screen: The size of the screen in inches.
Ads: The number of listings for 486 PCs’ prices for each month.

Let’s have a look at our randomly selected test dataset containing 1252 datapoints.

Table(head(testComputers_data))

Row.No	price	speed	hd	ram	screen	ads
3	1595	25	170	4	15	94
4	1849	25	170	8	14	94
7	1720	25	170	4	14	94
10	2575	50	210	4	15	94
11	2195	33	170	8	15	94
14	2295	25	245	8	14	94

The predictLayerHVT function is used to score the test data using the predictive set of maps. This function takes an input - a test data and a set of maps (map A, map B, map C).

Now, Let us understand the predictLayerHVT function.

predictLayerHVT(data,
                map_A,
                map_B,
                map_C,
                mad.threshold = 0.2,
                normalize = T, 
                distance_metric="L1_Norm",
                error_metric="max",
                child.level = 1, 
                line.width = c(0.6, 0.4, 0.2),
                color.vec = c("#141B41", "#6369D1", "#D8D2E1"),
                yVar= NULL,
                ...)

Each of the parameters of predictLayerHVT function has been explained below:

data - A dataframe containing the test dataset. The variables from this dataset can also be used to overlay as heatmap.
map A - A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on train data. This list containes information about the hierarchical vector quantized data along with a summary section.
map B - A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on data with novelty which was obtained as a result of removeNovelty function.
map C - A list of hvt.results.model obtained from HVT function while performing hierarchical vector quantization on data without novelty obtained as a result of removeNovelty function.
child.level - A number indicating the layer for which the heat map is to be plotted.(Only used if hmap.cols is not NULL).It specifies the specific layer within the hierarchical structure of the model.
mad.threshold - A threshold value used to filter data based on the Median Absolute Deviation (MAD) of the Quant.Error variable. It determines how extreme a deviation from the median has to be in order to be considered an novelty
normalize - A logical value indicating if the columns in the dataset should be normalized. If set to TRUE, the algorithm will normalize the data before performing compression. The default value is TRUE
distance_metric - It specifies the type of distance measurement used to calculate similarity or dissimilarity between data points.The distance calculation involves taking the difference between centroid_data and data_vec either by using Euclidean which calculates the sum of squared differences for each column and takes the square root or Manhattan which calculates the sum of absolute differences for each column. Euclidean is selected by default.
error_metric - It specifies the error metrics to be used for evaluating the performance of the model. It can be “mean” or “max”. If error_metric is set to mean, the code calculates the mean quantization error within each group while if the error_metric is not set to max, the code calculates the maximum quantization error within each group of columns. mean is selected by default.
yVar - Name of the dependent variable(s).
... - color.vec and line.width can be passed from here.

The function predicts based on the HVT maps - map A, map B and map C, constructed using HVT function. For each test record, the function will assign that record to Layer1 or Layer2. Layer1 contains the cell ids from map A and Layer 2 contains cell ids from map B (novelty map) and map C (map without novelty).

Prediction Algorithm

The prediction algorithm recursively calculates the distance between each point in the test dataset and the cell centroids for each level. The following steps explain the prediction method for a single point in the test dataset:

Calculate the distance between the point and the centroid of all the cells in the first level.
Find the cell whose centroid has minimum distance to the point.
Check if the cell drills down further to form more cells.
If it doesn’t, return the path. Or else repeat steps 1 to 4 till we reach a level at which the cell doesn’t drill down further.

Note : The prediction algorithm will not work if some of the variables used to perform quantization are missing. In the test dataset, we should not remove any features


validation_data <- testComputers
new_predict <- predictLayerHVT(
    data=validation_data,
    map_A,
    map_B,
    map_C,
    normalize = T
  )

Let’s see which cell and layer each point belongs to and check the Mean Absolute Difference for each of the 1252 records.

merged_df <- new_predict[["actual_predictedTable"]] %>%
  transmute(Row.Number,act_price,act_speed,act_hd,act_ram,act_screen,act_ads,Layer1.Cell.ID,Layer2.Cell.ID,
    pred_price = coalesce(price.x, price.y),
    pred_speed = coalesce(speed.x, speed.y),
    pred_hd = coalesce(hd.x, hd.y),
    pred_ram = coalesce(ram.x, ram.y),
    pred_screen = coalesce(screen.x, screen.y),
    pred_ads = coalesce(ads.x, ads.y)
  )


sorted_df <- merged_df
sorted_df$diff <- rowMeans(abs(sorted_df[, c("act_price","act_speed","act_hd","act_ram","act_screen","act_ads")] - sorted_df[, c("pred_price","pred_speed","pred_hd","pred_ram","pred_screen","pred_ads")]))
rownames(sorted_df) <- NULL
options(scipen = 999)
sorted_df %>% head(1000) %>%as.data.frame() %>%Table(scroll = T)

Row.Number	act_price	act_speed	act_hd	act_ram	act_screen	act_ads	Layer1.Cell.ID	Layer2.Cell.ID	pred_price	pred_speed	pred_hd	pred_ram	pred_screen	pred_ads	diff
1	-1.0786	-1.2710	-0.9473	-0.7590	0.4307	-1.7213	A24	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4690761
2	-0.6387	-1.2710	-0.9473	-0.0501	-0.6741	-1.7213	A143	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4423203
3	-0.8621	-1.2710	-0.9473	-0.7590	-0.6741	-1.7213	A29	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3712412
4	0.6187	-0.0817	-0.7914	-0.7590	0.4307	-1.7213	A237	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3987216
5	-0.0394	-0.8905	-0.9473	-0.0501	0.4307	-1.7213	A223	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3772698
6	0.1338	-1.2710	-0.6551	-0.0501	-0.6741	-1.7213	A162	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3302619
7	0.8334	-0.0817	-0.7836	-0.0501	-0.6741	-1.7213	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3328709
8	-0.2126	-0.8905	-0.6356	-0.7590	0.4307	-1.7213	A165	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4049180
9	0.9997	0.6795	-1.1031	-0.7590	-0.6741	-1.7213	A214	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.5427887
10	1.1382	-0.0817	-0.7914	-0.7590	2.6404	-1.7213	A379	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.7270886
11	3.0780	-0.8905	0.1513	-0.0501	-0.6741	-1.7213	A367	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.7243132
12	1.5193	-0.8905	-0.2850	1.3676	-0.6741	-1.7213	A316	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5546289
13	0.6533	-0.8905	-0.7914	-0.0501	2.6404	-1.7213	A343	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.6569314
14	0.3243	-0.0817	-0.7914	-0.0501	-0.6741	-1.7213	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2493209
15	0.3589	-0.0817	-0.7914	-0.0501	-0.6741	-1.7213	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2550875
16	0.4871	-0.8905	-0.7836	-0.0501	-0.6741	-1.7213	A187	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3049983
17	0.8265	-0.8905	-0.6551	-0.0501	-0.6741	-1.7213	A187	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3822952
18	-0.8119	-1.2710	-1.1420	-0.7590	-0.6741	-1.7213	A29	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4120579
19	1.3461	0.6795	-0.2850	-0.0501	0.4307	-1.7213	A330	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3298423
20	-0.7322	-0.8905	-0.9473	-0.7590	0.4307	-1.7213	A24	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4633928
21	-0.9054	-0.8905	-0.9473	-0.7590	-0.6741	-1.7213	A43	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3006079
22	1.4309	-0.0817	-0.6551	-0.0501	-0.6741	-1.7213	A215	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3679176
23	-1.0197	-1.2710	-1.2979	-0.0501	-0.6741	-1.7213	A10	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.5204834
24	0.6533	-1.2710	-0.6551	-0.0501	-0.6741	-1.7213	A187	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4168452
25	-0.3789	-0.8905	-1.1420	-0.0501	-0.6741	-1.7213	A143	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3680537
26	0.3502	-0.8905	-0.9473	-0.0501	0.4307	-1.7213	A223	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4077145
27	0.6533	-1.2710	-0.2850	1.3676	-0.6741	-1.7079	A316	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.6314504
28	-0.9054	-0.8905	-0.9473	-0.7590	-0.6741	-1.7079	A43	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2983746
29	1.3530	0.6795	-0.3240	-0.7590	0.4307	-1.7079	A330	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4511090
30	1.3461	0.6795	-0.6356	-0.0501	2.6404	-1.7079	A386	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5181657
31	-0.1260	0.6795	-0.9473	-0.7590	-0.6741	-1.7079	A166	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3903275
32	1.3461	0.6795	-0.2850	-0.0501	0.4307	-1.7079	A330	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3276090
33	-1.2448	-1.2710	-0.9473	-0.7590	-0.6741	-1.7079	A10	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3258541
34	0.1857	0.6795	-0.6356	-0.0501	-0.6741	-1.7079	A250	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3735345
35	0.0039	-0.8905	-0.6356	-0.7590	-0.6741	-1.7079	A121	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2683518
36	1.3530	-0.0817	-0.9473	-0.7590	-0.6741	-1.7079	A214	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4466054
37	-0.0394	-0.8905	-0.9473	-0.0501	0.4307	-1.7079	A223	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3750365
38	2.0458	0.6795	-0.7135	-0.7590	0.4307	-1.7079	A330	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5576488
39	0.6602	-0.0817	-0.7914	-0.7590	-0.6741	-1.7079	A214	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3051554
40	0.9114	0.6795	-0.6551	-0.0501	-0.6741	-1.7079	A250	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4014226
41	2.7316	-0.8905	0.1513	-0.0501	-0.6741	-1.7079	A367	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.6643465
42	0.9131	0.6795	-0.6356	-0.7590	0.4307	-1.7079	A292	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5763590
43	0.3139	-0.0817	-0.7836	-0.7590	-0.6741	-1.7079	A126	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2461387
44	0.7382	-0.0817	-0.6551	-0.0501	-0.6741	-1.7079	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3355012
45	0.6187	0.6795	-0.6356	-0.0501	-0.6741	-1.7079	A250	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4457012
46	0.8265	-0.8905	-0.2850	1.3676	-0.6741	-1.7079	A316	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.5391671
47	1.3530	0.6795	-0.6551	1.3676	0.4307	-1.6406	A370	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5052535
48	0.9824	-0.8905	-0.6356	-0.0501	2.6404	-1.6406	A343	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.6723648
49	-1.0786	-0.8905	-1.2784	-1.1134	-0.6741	-1.6406	A6	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3725412
50	2.7316	-0.8905	0.1513	-0.0501	-0.6741	-1.6406	A367	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.6531299
51	2.9048	0.6795	0.3383	-0.0501	0.4307	-1.6406	A382	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5878379
52	0.7226	-0.8905	-0.6356	-0.0501	2.6404	-1.6406	A343	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.6290648
53	0.6602	-0.0817	-0.7836	-0.0501	-0.6741	-1.6406	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2905542
54	0.9131	0.6795	-0.9473	-0.0501	-0.6741	-1.6406	A250	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4386226
55	1.1729	-0.0817	-0.2850	1.3676	-0.6741	-1.6406	A366	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4641122
56	-0.3789	-0.8905	-0.9473	-0.7590	-0.6741	-1.6406	A85	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3242382
57	-0.0394	-1.2710	-0.6551	-0.0501	-0.6741	-1.6406	A162	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3224339
58	-1.0786	-1.2710	-0.9473	-0.7590	0.4307	-1.6406	A24	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4556261
59	0.6187	0.6795	-0.6356	-0.0501	-0.6741	-1.6406	A250	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4344845
60	1.8726	0.6795	-0.6551	1.3676	0.4307	-1.6406	A370	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5180100
61	0.8334	0.6795	-0.7798	-0.0501	-0.6741	-1.6406	A250	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4239892
62	-0.8621	-1.2710	-0.9473	-0.7590	-0.6741	-1.6406	A29	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3577912
63	-0.3858	-0.8905	-0.6356	-0.7590	-0.6741	-1.6406	A85	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3220851
64	0.1407	-0.0817	-0.7836	-0.7590	-0.6741	-1.6406	A126	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2060554
65	0.5667	0.6795	-0.6356	-0.0501	0.4307	-1.6406	A292	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5047256
66	0.1164	-0.0817	-0.6356	-0.0501	-0.6741	-1.5331	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2059845
67	2.7316	-0.8905	0.1513	-0.0501	-0.6741	-1.5331	A367	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.6352132
68	0.6602	-0.0817	-0.7836	-0.0501	-0.6741	-1.5331	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2726375
69	1.1729	-0.0817	-0.2850	1.3676	-0.6741	-1.5331	A366	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4461955
70	-0.0394	0.6795	-0.9473	-0.7590	-0.6741	-1.5331	A166	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3467608
71	-0.9140	-0.8905	-1.2784	-1.1134	-0.6741	-1.5331	A6	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3820579
72	1.5106	0.6795	-0.2850	1.3676	-0.6741	-1.5331	A366	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3390671
73	0.6602	-0.8905	-0.6746	-0.7590	0.4307	-1.5331	A165	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4202959
74	-0.0394	-0.0817	-1.1031	-0.7590	-0.6741	-1.5331	A126	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2458608
75	0.6533	-0.0817	-0.6356	-0.0501	0.4307	-1.5331	A261	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3937173
76	0.5148	-0.8905	-0.6356	-0.0501	0.4307	-1.5331	A223	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4004781
77	-0.2473	0.6795	-0.9473	-0.7590	-0.6741	-1.5331	A166	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3814108
78	1.0066	-0.0817	-0.6746	-0.7590	0.4307	-1.5331	A237	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4481852
79	0.1338	-0.8905	-0.6551	-0.0501	-0.6741	-1.5331	A162	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2354786
80	-1.2604	-1.2710	-1.2784	-1.1134	-0.6741	-1.5331	A6	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4135708
81	-1.4249	-1.2710	-1.2784	-1.1134	-0.6741	-1.5331	A6	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4409875
82	0.4005	-0.8905	-0.7135	-0.7590	-0.6741	-1.5331	A121	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2722798
83	-0.5521	-0.8905	-0.7836	-0.7590	-0.6741	-1.1163	A104	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2313746
84	1.1642	-0.8905	0.1513	1.3676	-0.6741	-1.1163	A301	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.4148116
85	-0.0481	-0.8905	-0.7759	-0.7590	0.4307	-1.1163	A165	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3532458
86	-0.3858	-0.8905	-0.6356	-0.0501	-0.6741	-1.1163	A142	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3325784
87	0.4801	-0.0817	-0.6356	-0.0501	0.4307	-1.1163	A261	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3953451
88	0.3243	-0.0817	-0.6356	-0.0501	-0.6741	-1.1163	A215	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2711290
89	0.1338	-1.2710	-0.2850	-0.0501	-0.6741	-1.1163	A201	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3910731
90	1.0170	-0.8905	-0.6356	-0.0501	2.6404	-1.1163	A343	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.5907481
91	0.6602	-0.8905	-0.6551	1.3676	0.4307	-1.1163	A345	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.6451507
92	0.3069	-0.8905	-0.6356	-0.0501	-0.6741	-1.1163	A187	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2980731
93	0.3589	0.6795	-0.6356	-0.0501	0.4307	-1.1163	A292	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4502633
94	0.5148	-0.8905	-0.6356	-0.0501	0.4307	-1.1163	A226	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4309725
95	2.2120	0.6795	0.3383	-0.0501	-0.6741	-1.1163	A337	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3873014
96	-0.3165	-0.8905	-0.6356	-0.0501	-0.6741	-1.1163	A142	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3210284
97	0.6602	-0.0817	-0.0318	-0.7590	0.4307	-1.1163	A237	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.5280797
98	-0.0394	-0.0817	-0.7759	-0.7590	-0.6741	-1.1163	A152	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2237022
99	0.8178	-0.0817	-0.2850	-0.0501	0.4307	-1.1163	A261	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.4305373
100	1.3374	-0.0817	0.1513	1.3676	0.4307	-1.1163	A370	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.5289043
101	-0.0481	-0.8905	-0.7759	-0.7590	-0.6741	-1.1163	A104	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2549963
102	-0.1260	-0.0817	-0.9473	-0.7590	-0.6741	-1.1163	A152	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2648219
103	0.8178	-0.0817	-0.2850	-0.0501	-0.6741	-1.1163	A228	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3702176
104	1.6924	0.6795	0.4941	-0.0501	0.4307	-1.1163	A330	C201	1.5745305	0.4514353	-0.2367672	0.2737663	-0.3026593	-1.3041998	0.3869878
105	0.7399	-0.0817	-0.6356	-0.0501	0.4307	-1.1163	A261	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4386451
106	-0.3789	-0.8905	-0.9473	-0.7590	-0.6741	-1.1163	A104	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2875246
107	1.2422	-0.0817	-0.6356	-0.0501	2.6404	-1.1163	A379	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5235326
108	0.6187	-0.0817	-0.6356	-0.0501	0.4307	-1.1163	A261	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.4184451
109	-0.5521	-0.8905	-0.9473	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2009794
110	-0.2126	-0.8905	-0.6356	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2270755
111	-0.5677	0.6795	-1.1927	-1.1134	0.4307	-0.6189	A164	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.5411087
112	-1.2604	-0.8905	-1.1927	-1.1134	-0.6741	-0.6189	A33	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2087256
113	-0.2473	-0.0817	-0.6356	-0.0501	-0.6741	-0.6189	A193	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3625510
114	0.1338	-0.0817	-0.7836	-0.0501	0.4307	-0.6189	A261	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3965482
115	-0.0481	-0.8905	-0.7759	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2564128
116	-0.8621	-0.8905	-0.9473	-0.7590	-0.6741	-0.6189	A93	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1493128
117	-0.2057	-0.8905	-1.1420	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2911628
118	1.8137	-0.8905	0.4941	-0.0501	2.6404	-0.6189	A391	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.6040559
119	0.0472	-0.8905	-0.6356	-0.0501	0.4307	-0.6189	A226	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4054609
120	1.1642	-0.8905	0.1513	1.3676	0.4307	-0.6189	A340	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.4470952
121	0.1338	0.6795	-0.7759	-0.7590	-0.6741	-0.6189	A178	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4101140
122	-1.2085	-1.2710	-1.2979	-0.7590	-0.6741	-0.6189	A28	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2219590
123	0.9131	-0.8905	0.4941	-0.0501	-0.6741	-0.6189	A217	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4510949
124	0.1251	-0.0817	-0.7759	-0.7590	0.4307	-0.6189	A164	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.3977797
125	-0.3339	0.6795	-0.9473	-0.7590	-0.6741	-0.6189	A150	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3383333
126	0.3069	0.6795	-0.6356	-0.7590	-0.6741	-0.6189	A178	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4545569
127	-0.5521	-0.8905	-0.6356	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1704922
128	-0.5538	-0.8905	-1.1031	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2266628
129	1.3374	-0.0817	0.1513	1.3676	0.4307	-0.6189	A347	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.5091738
130	-0.5070	-0.0817	-0.9473	-0.7590	0.4307	-0.6189	A164	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4444743
131	-1.0786	-0.8905	-0.9473	-0.7590	0.4307	-0.6189	A86	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2471476
132	0.9651	-0.0817	-0.6356	-0.0501	2.6404	-0.6189	A379	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4906142
133	0.1251	-0.0817	-0.7759	-0.7590	-0.6741	-0.6189	A152	C404	0.0640661	-0.3965677	-0.7815408	-0.3982964	-0.4164485	-1.4161834	0.2995302
134	0.6602	-0.0817	0.1435	-0.0501	-0.6741	-0.6189	A248	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4054206
135	0.4109	-0.8905	-0.6356	-0.0501	2.6404	-0.6189	A284	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4068314
136	-0.7322	0.6795	-1.1927	-1.1134	-0.6741	-0.6189	A150	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4209190
137	2.3332	0.6795	0.4941	-0.0501	2.6404	-0.6189	A391	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.4345556
138	0.2203	-0.0817	-0.6356	-0.0501	0.4307	-0.6189	A263	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4213597
139	-1.4336	-1.2710	-1.1927	-1.1134	-0.6741	-0.6189	A28	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3010090
140	-1.1063	-1.2710	-1.1420	-0.7590	-0.6741	-0.6189	A28	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2044794
141	-0.2057	-0.8905	-1.1420	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2911628
142	1.5106	0.6795	0.1513	1.3676	0.4307	-0.6189	A370	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4111738
143	-0.7322	-1.2710	-0.7759	-0.7590	-0.6741	-0.6189	A107	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2058128
144	1.3530	0.6795	-0.2655	1.3676	0.4307	0.3625	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2908072
145	-0.5677	-1.2710	-0.7759	-0.7590	0.4307	0.3625	A119	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2319876
146	-0.0325	0.6795	-0.2850	-0.7590	-0.6741	0.3625	A180	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2891235
147	-0.2213	0.6795	-1.1927	-1.1134	-0.6741	0.3625	A140	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2649932
148	0.9114	-0.8905	0.1513	1.3676	-0.6741	0.3625	A291	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1262116
149	-0.5521	-0.8905	-0.7135	-0.7590	-0.6741	0.3625	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0803864
150	-0.5867	-0.0817	-0.7914	-0.7590	0.4307	0.3625	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2907700
151	-0.5677	-0.8905	-1.1927	-1.1134	-0.6741	0.3625	A64	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1709377
152	-0.8621	-0.8905	-0.7836	-0.7590	-0.6741	0.3625	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1203697
153	-0.9140	-0.8905	-1.1927	-1.1134	-0.6741	0.3625	A45	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2286543
154	-0.8119	-0.8905	-0.9473	-0.7590	-0.6741	0.3625	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1116710
155	-1.0872	-1.2710	-1.1927	-1.1134	-0.6741	0.3625	A27	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2591759
156	-0.1278	-0.0817	-1.1927	-1.1134	-0.6741	0.3625	A91	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3128782
157	1.0066	0.6795	0.1435	-0.0501	0.4307	0.3625	A297	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2650350
158	0.8334	-0.8905	-0.2655	1.3676	0.4307	0.3625	A322	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3048840
159	0.7382	-0.8905	-0.2850	-0.0501	-0.6741	0.3625	A175	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2403867
160	1.0066	-0.8905	-0.6746	-0.7590	-0.6741	0.3625	A163	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.3335879
161	0.3139	-0.0817	-0.2850	-0.0501	-0.6741	0.3625	A221	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2137795
162	0.3069	-0.0817	-0.2850	-0.0501	0.4307	0.3625	A238	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2052689
163	-0.7322	-0.8905	-1.1927	-1.1134	-0.6741	0.3625	A45	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1983543
164	-0.2126	-0.8905	-0.7759	-0.7590	-0.6741	0.3625	A109	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1135046
165	0.0125	0.6795	-0.6356	-0.7590	-0.6741	0.3625	A180	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2497140
166	-0.7322	-1.2710	-0.7759	-0.7590	-0.6741	0.3625	A87	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1359548
167	2.6017	0.6795	0.3383	-0.0501	-0.6741	0.3625	A300	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3364352
168	0.6533	0.6795	-0.2850	-0.0501	-0.6741	0.3625	A253	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1899740
169	0.6966	-0.0817	-0.2850	-0.0501	0.4307	0.3625	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2702189
170	0.3918	-0.8905	-0.2850	-0.0501	-0.6741	0.3625	A175	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1826534
171	1.5106	0.6795	0.1513	1.3676	-0.6741	0.3625	A354	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2854946
172	-0.2144	-0.8905	-0.7759	-0.7590	-0.6741	0.3625	A109	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1132046
173	0.7919	0.6795	-0.2850	-0.0501	2.6404	0.3625	A357	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3964270
174	1.3530	0.6795	-0.3629	-0.0501	0.4307	0.3625	A280	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3240300
175	1.3461	0.6795	0.1513	1.3676	-0.6741	0.3625	A354	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2580779
176	0.8178	0.6795	-0.2850	-0.0501	-0.6741	0.3625	A253	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2173907
177	0.9114	0.6795	-0.2850	-0.0501	-0.6741	0.3625	A251	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2329907
178	-0.3875	-1.2710	-0.7759	-0.7590	-0.6741	0.3625	A87	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1203063
179	-1.2518	-0.8905	-0.7836	-0.7590	-0.6741	0.3625	A65	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1570819
180	-0.2992	-0.8905	-0.6356	-0.0501	0.4307	0.3625	A167	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1841609
181	-0.0412	0.6795	-1.1927	-1.1134	-0.6741	0.3625	A140	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2950098
182	2.4094	0.6795	0.4434	-0.7590	0.4307	0.3625	A360	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3089082
183	-0.8188	-0.8905	-0.7914	-0.7590	-0.6741	0.3625	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1118531
184	2.3852	0.6795	0.3383	-0.0501	0.4307	0.3625	A360	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2196591
185	1.6041	-0.0817	0.1513	1.3676	-0.6741	0.3625	A326	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3539755
186	-1.2518	-0.8905	-0.7836	-0.7590	-0.6741	1.0212	A53	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1013950
187	0.2186	-0.8905	0.0500	-0.0501	-0.6741	1.0212	A168	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1496105
188	0.9980	-0.8905	0.4941	1.3676	-0.6741	1.0212	A288	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1655660
189	2.2189	0.6795	0.1435	-0.0501	2.6404	1.0212	A385	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.2438705
190	0.8265	0.6795	-0.2850	-0.7590	-0.6741	1.0212	A195	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2961906
191	-0.9054	-1.2710	-0.7759	-0.7590	-0.6741	1.0212	A56	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1740807
192	-0.7322	-0.0817	-1.1927	-1.1134	-0.6741	1.0212	A50	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2994962
193	-1.0716	-0.0817	-0.9473	-0.7590	-0.6741	1.0212	A51	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2514261
194	3.9422	-0.8905	2.6755	1.3676	-0.6741	1.0212	A423	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	1.0198327
195	0.3918	-0.8905	0.0500	-0.0501	-0.6741	1.0212	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1784771
196	0.6429	0.6795	-0.2850	-0.0501	2.6404	1.0212	A336	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4039419
197	0.4784	-0.0817	-1.1927	-0.7590	-0.6741	1.0212	A128	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3931939
198	-0.6110	-1.2710	-0.9473	-0.7590	2.6404	1.0212	A1	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.5007937
199	-0.0741	0.6795	-0.6356	-0.0501	0.4307	1.0212	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2503874
200	-0.7409	-0.8905	-1.1927	-1.1134	0.4307	1.0212	A15	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3560236
201	-0.9054	-0.8905	-0.6356	-0.7590	-0.6741	1.0212	A72	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1837950
202	1.0863	0.6795	-0.2850	-0.0501	2.6404	1.0212	A357	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4467318
203	1.3530	0.6795	0.4941	-0.0501	0.4307	1.0212	A297	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3180865
204	2.2120	-0.0817	0.1513	-0.0501	-0.6741	1.0212	A257	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3904274
205	0.6447	-1.2710	0.4941	1.3676	-0.6741	1.0212	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2400214
206	0.1338	0.6795	-0.6356	-0.0501	0.4307	1.0212	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2157374
207	-1.2604	-1.2710	-1.1927	-1.1134	0.4307	1.0212	A15	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3323876
208	1.3443	-0.8905	0.4941	1.3676	-0.6741	1.0212	A299	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2232827
209	-2.0242	-0.8905	-1.2979	-1.1134	-0.6741	1.0212	A5	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3023338
210	0.6533	-0.8905	0.4941	1.3676	-0.6741	1.0212	A288	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1751714
211	-0.5607	-0.8905	-1.1927	-1.1134	-0.6741	1.0212	A48	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2117014
212	0.1407	0.6795	-1.1420	-0.7590	-0.6741	1.0212	A155	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2961588
213	-1.2518	-0.8905	-1.1927	-1.1134	-0.6741	1.0212	A32	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1623866
214	0.8248	-1.2710	0.4941	1.3676	-0.6741	1.0212	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2100048
215	-1.2448	-0.8905	-1.1031	-0.7590	-0.6741	1.0212	A47	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1137261
216	-0.3858	-1.2710	0.0500	-0.0501	-0.6741	1.0212	A159	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2066780
217	3.0849	0.6795	0.1435	-0.0501	2.6404	1.0212	A394	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3882039
218	3.2511	0.6795	0.3383	1.3676	2.6404	1.0212	A413	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4303634
219	-1.0716	0.6795	-1.1031	-0.7590	-0.6741	1.0212	A67	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2782680
220	1.3374	-0.0817	0.4941	1.3676	-0.6741	1.0212	A333	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3344465
221	-0.3512	0.6795	-0.7914	-0.7590	-0.6741	1.0212	A137	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1729729
222	3.7707	-0.0817	2.6755	1.3676	-0.6741	1.0212	A423	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	1.0072337
223	0.8334	-0.8905	-0.2655	1.3676	0.4307	1.0212	A323	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3620979
224	-0.5590	-0.8905	-0.7836	-0.0501	0.4307	1.0212	A172	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2243043
225	1.4309	-0.8905	0.4941	1.3676	-0.6741	1.0212	A299	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2377160
226	0.6602	-0.0817	-0.7135	-0.7590	-0.6741	1.0212	A128	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3868246
227	-0.8119	-0.8905	-0.7135	-0.7590	-0.6741	1.0212	A72	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1656169
228	-0.5607	-1.2710	-0.7759	-0.0501	-0.6741	1.0212	A79	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2660491
229	1.0066	0.6795	0.1435	-0.0501	0.4307	1.0212	A297	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2771349
230	-0.3512	-0.8905	-0.6356	-0.0501	0.4307	1.0212	A172	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2055469
231	1.1729	-0.0817	0.4941	1.3676	-0.6741	1.0212	A333	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3070299
232	0.1251	0.6795	-0.7759	-0.7590	0.4307	1.0212	A173	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3213519
233	-0.3858	-0.8905	-0.7759	-0.7590	-0.6741	1.0212	A88	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1265684
234	2.3852	0.6795	0.1513	-0.0501	-0.6741	1.0212	A300	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3234441
235	-0.4741	-0.0817	-1.1927	-1.1134	-0.6741	1.0212	A91	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2650037
236	0.2446	-0.8905	-0.2850	-0.0501	2.6404	1.0212	A255	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2192758
237	0.9114	0.6795	0.0500	-0.0501	-0.6741	1.0212	A239	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2297864
238	0.1338	-0.0817	-0.2850	-0.0501	0.4307	0.3222	A238	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2204931
239	-0.2057	-0.8905	-1.1420	-0.7590	-0.6741	0.3222	A98	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1522058
240	0.1338	-0.8905	0.0500	-0.0501	-0.6741	0.3222	A188	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0905367
241	-1.2604	-1.2710	-1.1927	-1.1134	-0.6741	0.3222	A27	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2370259
242	0.4871	-0.0817	-0.2850	-0.0501	0.4307	0.3222	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2420189
243	-0.3789	0.6795	-0.9473	-0.7590	-0.6741	0.3222	A125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1739833
244	0.6516	-0.0817	0.0500	-0.0501	-0.6741	0.3222	A221	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2315539
245	-0.8119	-0.0817	-0.7836	-0.7590	-0.6741	0.3222	A94	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2307415
246	-0.5001	0.6795	-0.7914	-0.7590	0.4307	0.3222	A197	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2927796
247	-0.4222	-1.2710	-1.1420	-0.7590	-0.6741	0.3222	A60	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1520742
248	-0.7079	-0.0817	-0.7914	-0.7590	0.4307	0.3222	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3176866
249	0.0454	-0.8905	0.0500	-0.0501	-0.6741	0.3222	A188	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0758034
250	-1.4249	-1.2710	-0.9473	-0.7590	0.4307	0.3222	A76	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3429611
251	-0.0325	-0.0817	-0.7135	-0.0501	0.4307	0.3222	A191	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3196264
252	-0.9054	-0.8905	-1.1927	-1.1134	-0.6741	0.3222	A45	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2339377
253	-0.6473	-1.2710	-0.7759	-0.7590	-0.6741	0.3222	A87	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1285215
254	3.2511	0.6795	0.3383	1.3676	2.6404	0.3222	A426	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3935297
255	-0.9054	-1.2710	-0.7759	-0.7590	-0.6741	0.3222	A55	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1715381
256	0.4351	-0.8905	-0.6551	-0.0501	-0.6741	0.3222	A175	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2582700
257	-0.3010	-0.8905	-0.7759	-0.7590	-0.6741	0.3222	A109	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1054879
258	0.0454	-0.0817	-0.7759	-0.7590	-0.6741	0.3222	A147	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2708324
259	0.6256	-0.8905	-0.2850	-0.7590	-0.6741	0.3222	A144	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3224366
260	0.2186	-0.8905	-1.1420	-0.7590	-0.6741	0.3222	A98	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2229225
261	1.6907	0.6795	0.0422	1.3676	0.4307	0.3222	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3025238
262	0.1926	-0.8905	-0.2850	-0.0501	2.6404	0.3222	A255	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.1993171
263	-1.0872	-0.8905	-1.1927	-1.1134	0.4307	0.3222	A40	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3904042
264	0.4853	-0.0817	-0.7135	-0.7590	-0.6741	0.3222	A147	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3545491
265	-1.0716	-1.2710	-1.1420	-0.7590	-0.6741	0.3222	A36	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2251487
266	-0.8985	0.6795	-0.9473	-0.7590	-0.6741	0.3222	A125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2203258
267	-2.1108	-0.8905	-1.2979	-1.1134	-0.6741	0.3222	A14	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3791708
268	-0.6473	-0.8905	-1.1927	-1.1134	-0.6741	0.3222	A64	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1909210
269	0.4715	-0.0817	0.0500	-0.0501	-0.6741	0.3222	A221	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2015373
270	4.8168	0.6795	0.4356	-0.0501	2.6404	0.3222	A394	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.6887035
271	1.3374	-0.0817	0.4473	1.3676	0.4307	0.3222	A347	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3305479
272	1.2577	-0.8905	0.4473	1.3676	-0.6741	0.3222	A309	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1505521
273	0.6447	-1.2710	0.4473	1.3676	-0.6741	0.3222	A285	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1817242
274	-0.7772	-0.8905	-0.9473	-0.7590	-0.6741	0.3222	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1126043
275	1.3461	0.6795	0.0422	1.3676	0.4307	0.3222	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2450905
276	0.8178	-0.0817	-0.2850	-0.0501	0.4307	0.3222	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2971355
277	1.1798	-0.0817	-0.3629	-0.0501	0.4307	0.3222	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3704522
278	1.6041	-0.0817	0.4473	1.3676	-0.6741	0.3222	A326	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3205993
279	1.3461	0.6795	0.4473	1.3676	-0.6741	0.3222	A354	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2430187
280	-0.2144	0.6795	-1.1927	-1.1134	-0.6741	0.3222	A140	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2728598
281	1.5106	0.6795	0.4473	1.3676	0.4307	0.3222	A373	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2325479
282	0.8178	-0.8905	0.4473	1.3676	0.4307	0.3222	A315	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2046411
283	1.1729	-0.0817	0.4473	1.3676	-0.6741	0.3222	A326	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2487326
284	-0.5677	-0.8905	-0.7759	-0.7590	-0.6741	0.3222	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0793031
285	0.6533	-0.8905	0.4473	1.3676	-0.6741	0.3222	A294	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1168742
286	-0.2126	-0.8905	-0.2850	-0.0501	0.4307	0.3222	A181	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1953865
287	5.5095	0.6795	3.0651	4.2032	2.6404	0.8196	A440	B12	5.5095223	0.6794579	3.0650965	4.2031619	2.6404120	0.4969247	0.0537989
288	-0.2213	-0.8905	-0.7759	-0.7590	0.4307	0.8196	A122	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1911328
289	-0.0394	-0.8905	-0.7836	-0.7590	2.6404	0.8196	A11	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2812270
290	0.6447	0.6795	0.0500	-0.0501	0.4307	0.8196	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1676349
291	-0.8811	-1.2710	-1.1420	-0.7590	-0.6741	0.8196	A39	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1739987
292	0.2186	-0.0817	-0.2850	-0.0501	0.4307	0.8196	A238	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1781430
293	-0.0481	-0.8905	0.0500	-0.0501	-0.6741	0.8196	A168	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0756189
294	2.0215	0.6795	3.7273	-0.0501	-0.6741	0.8196	A410	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.8079483
295	-1.4249	-0.8905	-0.7836	-0.7590	-0.6741	0.8196	A54	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1034291
296	0.9114	-1.2710	0.4473	1.3676	-0.6741	0.8196	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1731494
297	0.1338	-0.8905	0.0500	-0.0501	-0.6741	0.8196	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1018771
298	-0.8985	0.6795	-0.9473	-0.7590	-0.6741	0.8196	A125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1898513
299	-1.4249	-1.2710	-1.1927	-1.1134	-0.6741	0.8196	A18	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1780898
300	0.2533	-0.8905	-0.7836	-0.7590	-0.6741	0.8196	A106	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1982017
301	0.6447	-1.2710	0.4473	1.3676	-0.6741	0.8196	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1986214
302	0.2706	-0.8905	-1.1420	-0.7590	-0.6741	0.8196	A97	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2332029
303	-0.0481	-0.0817	-0.7759	-0.7590	-0.6741	0.8196	A128	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2247746
304	-0.9054	-1.2710	-0.7759	-0.7590	-0.6741	0.8196	A56	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1535842
305	-0.2126	0.6795	-0.2850	-0.7590	0.4307	0.8196	A173	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2622186
306	-0.3945	0.6795	-1.1927	-1.1134	-0.6741	0.8196	A114	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2123687
307	0.1320	-0.8905	0.0500	-0.0501	-0.6741	0.8196	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1015771
308	1.6041	0.6795	0.4473	1.3676	-0.6741	0.8196	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2438654
309	0.3069	-0.0817	-0.2850	-0.7590	2.6404	0.8196	A320	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.1853997
310	-0.7253	-0.8905	-1.1420	-0.7590	-0.6741	0.8196	A61	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1380181
311	-0.9937	-1.2710	-1.1927	-1.1134	-0.6741	0.8196	A18	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1985759
312	0.3918	-0.8905	0.0500	-0.0501	-0.6741	0.8196	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1448771
313	0.8438	0.6795	0.5136	-0.0501	0.4307	0.8196	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2780849
314	-1.5981	-1.2710	-0.9473	-0.7590	-0.6741	0.8196	A22	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1311625
315	-0.2126	-0.0817	-0.7759	-0.7590	-0.6741	0.8196	A128	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1973579
316	0.1320	-0.0817	-0.7759	-0.7590	-0.6741	0.8196	A128	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2547913
317	0.9633	-0.8905	-0.2850	-0.7590	-0.6741	0.8196	A144	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3900604
318	-0.7079	-0.0817	-0.7914	-0.7590	0.4307	0.8196	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3074226
319	-0.7495	-0.8905	-0.2850	-0.7590	0.4307	0.8196	A141	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1620159
320	1.6907	0.6795	0.4473	1.3676	-0.6741	0.8196	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2582987
321	1.5106	0.6795	0.4473	1.3676	-0.6741	0.8196	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2282820
322	0.9114	-0.8905	0.4473	1.3676	-0.6741	0.8196	A288	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1097327
323	0.8854	0.6795	-0.6551	-0.7590	0.4307	0.8196	A262	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3540777
324	0.3069	0.6795	-0.2850	-0.0501	0.4307	0.8196	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1151965
325	-1.2604	-1.2710	-1.1927	-1.1134	-0.6741	0.8196	A18	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1541259
326	-0.5521	-0.0817	-0.2850	-0.7590	-0.6741	0.8196	A132	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2400671
327	0.0454	-0.8905	0.0500	-0.0501	-0.6741	0.8196	A168	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0871438
328	-0.5590	0.6795	-1.1927	-1.1134	-0.6741	0.8196	A95	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2047279
329	-0.5590	-1.2710	-0.9473	-0.7590	2.6404	0.8196	A11	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4585270
330	2.4787	0.6795	0.1435	-0.0501	0.4307	0.8196	A329	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2260346
331	-1.4180	-0.8905	-0.7836	-0.7590	-0.6741	0.8196	A54	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1022791
332	-0.3858	0.6795	-0.2850	-0.7590	-0.6741	0.8196	A154	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2180063
333	0.7901	-0.8905	-0.6551	-0.0501	-0.6741	0.8196	A175	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3287771
334	-0.3945	-0.8905	-0.7836	-0.7590	-0.6741	0.8196	A99	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0902350
335	1.3374	-0.0817	0.4473	1.3676	-0.6741	0.8196	A333	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2930465
336	1.6976	0.6795	0.1435	1.3676	0.4307	0.8196	A359	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2446372
337	-0.7426	-0.8905	-0.6356	-0.7590	-0.6741	0.8196	A83	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1334502
338	-0.1278	-0.8905	-0.7759	-0.7590	-0.6741	0.8196	A106	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1359684
339	-1.1756	-1.2710	-0.7836	-0.7590	-0.6741	0.8196	A35	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1072676
340	0.8334	-0.8905	-0.6746	-0.7590	-0.6741	0.8196	A144	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.3130517
341	-1.3314	-1.2710	-0.7836	-0.7590	-0.6741	0.8196	A35	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1015148
342	-1.5981	-1.2710	-0.7836	-0.7590	-0.6741	0.4969	A37	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1997481
343	-0.9140	-1.2710	-0.7836	-0.7590	-0.6741	0.4969	A55	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1425715
344	-0.4395	-1.2710	-1.1420	-0.7590	-0.6741	0.4969	A60	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1200742
345	-0.0481	-0.0817	-1.1927	-1.1134	-0.6741	0.4969	A91	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3037615
346	2.2969	0.6795	-0.2850	1.3676	2.6404	0.4969	A413	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.2338978
347	-0.5070	0.6795	-0.2850	-0.7590	0.4307	0.4969	A233	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3103854
348	-0.6802	0.6795	-0.2850	-0.7590	-0.6741	0.4969	A154	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2304732
349	0.0454	-0.0817	-0.7759	-0.7590	-0.6741	0.4969	A147	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2417157
350	-0.2057	-0.8905	-1.1420	-0.7590	-0.6741	0.4969	A98	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1230892
351	-0.9850	-1.2710	-1.1420	-0.7590	-0.6741	0.4969	A36	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1865227
352	0.4871	0.6795	0.0422	-0.0501	0.4307	0.4969	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1391683
353	-0.0862	-0.8905	-0.7836	-0.7590	-0.6741	0.4969	A118	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1108879
354	0.8092	0.6795	0.5136	-0.0501	-0.6741	0.4969	A253	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2569256
355	-0.3010	0.6795	-1.1927	-1.1134	-0.6741	0.4969	A112	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2293098
356	0.1164	0.6795	-0.2850	-0.0501	0.4307	0.4969	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1257042
357	0.2533	-0.8905	-0.7836	-0.7590	-0.6741	0.4969	A118	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1674712
358	-0.6473	-1.2710	-0.7759	-0.7590	-0.6741	0.4969	A87	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0994048
359	-0.7253	-0.8905	-0.7135	-0.7590	-0.6741	0.4969	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0868531
360	0.3831	-0.0817	-0.2850	-0.0501	2.6404	0.4969	A320	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.1790270
361	-0.9054	-1.2710	-0.7759	-0.7590	-0.6741	0.4969	A55	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1424215
362	1.6041	-0.0817	0.4473	1.3676	-0.6741	0.4969	A326	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2914826
363	-0.0568	-1.2710	0.4473	-0.0501	-0.6741	0.4969	A160	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1438124
364	0.4853	-0.0817	-0.3629	-0.0501	0.4307	0.4969	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2255855
365	-0.8119	-0.0817	-0.6551	-0.7590	0.4307	0.4969	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2831866
366	2.0302	0.6795	0.4473	1.3676	-0.6741	0.4969	A368	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3279187
367	0.2446	-0.8905	-0.6551	-0.0501	-0.6741	0.4969	A175	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1974034
368	-0.2923	-0.8905	-0.7836	-0.7590	-0.6741	0.4969	A109	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0765379
369	-0.0481	-0.8905	-0.7759	-0.7590	-0.6741	0.4969	A118	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1185212
370	0.2186	0.6795	-0.7759	-0.7590	-0.6741	0.4969	A179	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2382807
371	-0.9054	-0.8905	-0.2850	-0.7590	0.4307	0.4969	A141	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1691465
372	0.1407	0.6795	-1.1420	-0.7590	-0.6741	0.4969	A140	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2639166
373	2.0458	0.6795	0.4434	-0.7590	0.4307	0.4969	A329	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2259082
374	-1.2604	-1.2710	-1.1927	-1.1134	0.4307	0.4969	A15	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3656745
375	-0.8985	-1.2710	-0.7836	-0.7590	-0.6741	0.4969	A55	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1399881
376	0.4715	-0.0817	-0.7759	-0.7590	-0.6741	0.4969	A147	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3127324
377	-0.3858	-1.2710	0.0500	-0.0501	-0.6741	0.4969	A160	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1326209
378	0.1338	0.6795	-0.2850	-0.0501	0.4307	0.4969	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1228042
379	0.8178	-0.8905	0.0500	-0.0501	-0.6741	0.4969	A188	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1754200
380	1.0915	0.6795	-0.6551	-0.7590	0.4307	0.4969	A262	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3875278
381	-1.2518	-0.8905	-0.7836	-0.7590	-0.6741	0.4969	A63	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1346819
382	0.9114	-1.2710	0.4473	1.3676	-0.6741	0.4969	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1271354
383	-1.2518	-0.8905	-0.7836	-0.7590	-0.6741	0.4969	A63	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1346819
384	-0.7409	-1.2710	-1.1927	-1.1134	-0.6741	0.4969	A27	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2133561
385	-0.5677	-0.0817	-1.1927	-1.1134	0.4307	0.4969	A68	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3874437
386	-0.8811	-1.2710	-1.1420	-0.7590	-0.6741	0.4969	A60	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1692061
387	0.4109	-0.8905	0.5136	-0.0501	-0.6741	0.4969	A202	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1846782
388	-0.2577	-0.8905	-0.2850	-0.7590	-0.6741	0.7120	A127	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1782017
389	-0.7166	-1.2710	-1.1420	-0.7590	-0.6741	0.7120	A57	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1545865
390	-1.1149	-1.2710	-0.7836	-0.7590	-0.6741	0.7120	A55	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1353176
391	-0.3789	-0.8905	-0.6356	-0.7590	-0.6741	0.7120	A99	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0995684
392	2.9117	0.6795	0.4434	1.3676	2.6404	0.7120	A413	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3397801
393	-0.0325	-0.0817	-0.2850	-0.0501	0.4307	0.7120	A238	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2020596
394	0.7382	0.6795	0.0500	-0.0501	-0.6741	0.7120	A253	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1493864
395	-0.4741	-1.2710	-0.7759	-0.7590	-0.6741	0.7120	A70	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0962701
396	0.7555	-0.8905	-0.2850	-0.7590	-0.6741	0.7120	A144	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3374937
397	5.5095	0.6795	3.0651	4.2032	2.6404	0.7120	A440	B12	5.5095223	0.6794579	3.0650965	4.2031619	2.6404120	0.4969247	0.0358656
398	1.2577	-0.0817	0.4473	1.3676	-0.6741	0.7120	A314	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2618299
399	0.9824	-0.8905	2.5197	-0.0501	-0.6741	0.7120	A390	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.6368019
400	0.1770	-0.8905	0.4473	-0.0501	-0.6741	0.7120	A202	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1571686
401	0.9114	-1.2710	0.4473	1.3676	-0.6741	0.7120	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1552160
402	-0.3356	-1.2710	-0.9473	-0.7590	-0.6741	0.7120	A84	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1177380
403	-0.7253	-1.2710	0.0422	-0.7590	-0.6741	0.7120	A108	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2615519
404	-0.5677	-0.8905	-0.7759	-0.7590	0.4307	0.7120	A101	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1470902
405	0.1251	-0.8905	-0.7759	-0.7590	-0.6741	0.7120	A118	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1601850
406	0.0454	-1.2710	0.0500	-0.0501	-0.6741	0.7120	A159	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1103862
407	-0.0481	-0.8905	-0.7759	-0.7590	-0.6741	0.7120	A106	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1313184
408	0.4715	-0.8905	0.0500	-0.0501	-0.6741	0.7120	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1402271
409	-0.5521	-0.8905	-0.6551	-0.7590	0.4307	0.7120	A101	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1243569
410	0.7087	0.6795	0.0422	-0.0501	0.4307	0.7120	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1590682
411	1.3461	0.6795	-0.2850	1.3676	0.4307	0.7120	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2395372
412	1.3530	0.6795	-0.2850	-0.0501	0.4307	0.7120	A280	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2716132
413	-0.4655	-0.0817	-0.6551	-0.7590	0.4307	0.7120	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2288152
414	-0.2750	-0.8905	-0.7836	-0.7590	-0.6741	0.7120	A99	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0922184
415	3.0780	0.6795	2.2860	1.3676	-0.6741	0.7120	A423	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.7780320
416	0.9824	-0.8905	0.4941	-0.0501	2.6404	0.7120	A255	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4205591
417	0.4801	0.6795	0.0422	-0.0501	0.4307	0.7120	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1209682
418	0.0870	-0.8905	-0.2850	-0.7590	-0.6741	0.7120	A144	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2260771
419	-1.2518	-0.8905	-0.7836	-0.7590	-0.6741	0.7120	A63	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.0988319
420	0.2273	0.6795	-0.2850	-0.7590	-0.6741	0.7120	A195	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2049844
421	-1.2604	-1.2710	-1.1927	-1.1134	0.4307	0.7120	A15	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3298245
422	1.3288	0.6795	0.4941	-0.0501	2.6404	0.7120	A357	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3522304
423	-0.3010	0.6795	-1.1927	-1.1134	-0.6741	0.7120	A114	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2100187
424	-0.0394	-0.8905	0.0500	-0.0501	-0.6741	0.7120	A168	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0562356
425	-0.4205	0.6795	-0.2850	-0.7590	0.4307	0.7120	A173	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2789353
426	-0.5677	-0.8905	-1.1927	-1.1134	-0.6741	0.7120	A48	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1613348
427	0.3624	-0.0817	0.0422	-0.0501	0.4307	0.7120	A238	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1699238
428	2.9048	-0.0817	2.2860	1.3676	-0.6741	0.7120	A423	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.7569912
429	1.1642	-0.0817	0.4473	1.3676	0.4307	0.7120	A347	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2942383
430	-1.2518	-0.8905	-1.1927	-1.1134	-0.6741	0.7120	A31	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1598236
431	-0.8985	0.6795	-0.6551	-0.7590	-0.6741	0.7120	A125	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1858821
432	-1.0716	-1.2710	-0.7836	-0.7590	-0.6741	0.7120	A55	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1425342
433	-0.6387	0.6795	-0.6551	-0.7590	0.4307	0.7120	A173	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2901885
434	-0.5590	-0.8905	-0.7759	-0.7590	-0.6741	0.7120	A90	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0615335
435	-1.5981	-1.2710	-0.7759	-0.7590	-0.6741	0.7120	A22	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1651814
436	-1.4596	-1.2710	-1.1420	-0.7590	-0.6741	0.7120	A26	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1584625
437	-1.2448	-0.8905	-0.6551	-0.7590	-0.6741	0.7120	A63	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1214152
438	2.5913	0.6795	0.4434	-0.0501	0.4307	0.7120	A360	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2569721
439	-0.7253	-0.8905	-1.1420	-0.7590	-0.6741	0.7120	A59	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1200848
440	0.1338	-0.0817	0.0422	-0.0501	0.4307	0.7120	A238	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1691813
441	-0.9175	-1.2710	-0.7836	-0.7590	-0.6741	0.7120	A70	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1559519
442	-0.9054	-0.8905	-0.2850	-0.0501	-0.6741	0.7120	A115	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2564023
443	-0.1711	-1.2710	-1.1420	-0.7590	-0.6741	0.7120	A57	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1776047
444	1.3288	0.6795	2.5197	-0.0501	-0.6741	0.7120	A410	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.6594364
445	-0.2040	-1.2710	0.0422	-0.7590	2.6404	0.7120	A11	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3553898
446	-1.5912	-1.2710	-1.1420	-0.7590	-0.6741	0.7120	A26	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1803959
447	1.6994	0.6795	-0.2850	1.3676	0.4307	0.7120	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2984206
448	-0.4741	-0.0817	-1.1927	-1.1134	-0.6741	0.7120	A91	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2134704
449	0.9114	-0.8905	0.4473	1.3676	-0.6741	0.7120	A288	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.0917994
450	0.8334	0.6795	-0.6551	-0.0501	0.4307	0.7120	A262	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2466965
451	-0.1278	-0.0817	-0.7759	-0.7590	-0.6741	0.7120	A128	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1935579
452	-0.7790	-0.8905	-1.1420	-0.7590	-0.6741	0.7120	A59	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1290348
453	-0.1278	-0.8905	-0.7759	-0.7590	-0.6741	0.7120	A99	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1180350
454	0.1251	0.6795	-0.7759	-0.7590	0.4307	-0.0812	A197	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3832020
455	-0.9937	-1.2710	-1.1927	-1.1134	-0.6741	-0.0812	A27	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3487092
456	1.5175	0.6795	-0.2850	-0.7590	-0.6741	-0.0812	A209	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4746131
457	0.6447	-0.8905	0.0500	-0.0501	-0.6741	-0.0812	A217	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2429200
458	-0.6473	-0.0817	-1.1927	-1.1134	-0.6741	-0.0812	A113	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3494540
459	0.9910	-0.8905	0.4473	1.3676	0.4307	-0.0812	A323	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2885190
460	1.0846	-0.8905	0.4473	1.3676	-0.6741	-0.0812	A310	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1889354
461	2.5653	0.6795	0.4434	1.3676	2.6404	-0.0812	A413	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3639797
462	-1.0872	-0.8905	-1.1927	-1.1134	0.4307	-0.0812	A40	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4352976
463	0.2896	0.6795	0.0266	-0.0501	2.6404	-0.0812	A344	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.2572833
464	0.8178	-0.0817	0.0500	-0.0501	-0.6741	-0.0812	A248	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3264873
465	0.2983	-0.0817	-0.7759	-0.7590	-0.6741	-0.0812	A147	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3802157
466	0.3104	0.6795	-0.5577	-0.7590	-0.6741	-0.0812	A180	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3513735
467	-0.0325	-0.8905	-0.2850	-0.0501	2.6404	-0.0812	A249	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3040671
468	-0.3945	-0.8905	-1.1927	-1.1134	-0.6741	-0.0812	A64	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2554892
469	0.0714	-0.8905	-0.6551	-0.7590	-0.6741	-0.0812	A145	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2549212
470	0.8750	-0.8905	-0.2850	-0.7590	-0.6741	-0.0812	A145	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4312366
471	-1.5981	-1.2710	-1.1927	-0.7590	-0.6741	-0.0812	A23	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3221959
472	0.6499	-0.0817	-0.7836	-0.7590	-0.6741	-0.0812	A147	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4375324
473	0.8334	0.6795	0.4473	-0.0501	0.4307	-0.0812	A273	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3607516
474	-0.5365	-0.8905	-1.1420	-0.7590	-0.6741	-0.0812	A58	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1721710
475	-1.0197	-0.0817	-0.6551	-0.7590	0.4307	-0.0812	A148	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4141700
476	0.0454	-1.2710	0.0500	-0.0501	-0.6741	-0.0812	A199	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1842124
477	-0.5607	-0.0817	-0.2850	-0.7590	0.4307	-0.0812	A148	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3244956
478	1.1261	0.6795	-0.2850	-0.7590	-0.6741	-0.0812	A209	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4093798
479	-0.8985	-0.8905	-1.1420	-0.7590	-0.6741	-0.0812	A58	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2325043
480	1.3461	0.6795	0.4473	1.3676	-0.6741	-0.0812	A354	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3102520
481	-1.0786	-0.8905	0.0266	-0.7590	-0.6741	-0.0812	A117	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2827255
482	1.3530	0.6795	0.1435	1.3676	0.4307	-0.0812	A373	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2965905
483	0.5650	-0.0817	0.0500	-0.0501	-0.6741	-0.0812	A248	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2843539
484	1.5227	0.6795	0.4473	-0.0501	0.4307	-0.0812	A297	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.3189734
485	1.0066	0.6795	-0.2850	1.3676	0.4307	-0.0812	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4154509
486	-0.6473	-1.2710	-0.7759	-0.7590	-0.6741	-0.0812	A110	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1957548
487	-0.9054	-1.2710	-0.7759	-0.7590	-0.6741	-0.0812	A93	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2387715
488	-0.7253	-0.8905	-1.1420	-0.7590	-0.6741	-0.0812	A58	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2036377
489	-1.3730	-1.2710	-1.1420	-0.7590	-0.6741	-0.0812	A23	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2762292
490	0.1060	-0.0817	-0.2850	-0.0501	2.6404	-0.0812	A344	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2723999
491	-0.0394	-0.8905	0.0500	-0.0501	-0.6741	-0.0812	A199	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1300619
492	0.2186	-0.8905	0.0500	-0.0501	-0.6741	-0.0812	A199	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1719034
493	0.1926	0.6795	-0.7836	-0.7590	-0.6741	0.9406	A155	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2514729
494	1.5106	0.6795	0.4473	1.3676	0.4307	0.9406	A359	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2105613
495	1.0066	-0.0817	-0.7135	-0.0501	0.4307	0.9406	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3919688
496	1.5106	0.6795	0.4473	1.3676	-0.6741	0.9406	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2484487
497	-0.2144	-0.0817	0.0422	-0.7590	-0.6741	0.9406	A156	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3505476
498	-1.4180	0.6795	-0.5577	-0.7590	-0.6741	0.9406	A102	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3267987
499	1.1625	-0.8905	-0.2850	-0.7590	2.6404	0.9406	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3567502
500	-1.1669	-0.8905	-1.1927	-1.1134	-0.6741	0.9406	A32	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1631033
501	0.3069	0.6795	-0.2850	-0.0501	0.4307	0.9406	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1353632
502	-1.5912	-0.8905	-0.7836	-0.7590	-0.6741	0.9406	A42	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1382089
503	0.2186	-0.8905	0.0500	-0.0501	-0.6741	0.9406	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1361771
504	-0.7322	-0.0817	-0.7759	-0.7590	-0.6741	0.9406	A103	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2084337
505	0.1320	-0.0817	0.0422	-0.0501	-0.6741	0.9406	A204	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2261585
506	-1.4700	-1.2710	-1.1420	-0.7590	-0.6741	0.9406	A26	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1493256
507	0.3589	-0.0817	-0.7836	-0.0501	-0.6741	0.9406	A128	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2866403
508	-1.2535	-0.8905	-0.6746	-0.7590	0.4307	0.9406	A101	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2636102
509	0.5044	-0.0817	-0.2850	-0.7590	0.4307	0.9406	A185	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3176333
510	1.6041	0.6795	0.4473	1.3676	-0.6741	0.9406	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2640320
511	0.4801	-1.2710	0.4473	1.3676	-0.6741	0.9406	A274	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2462214
512	-1.0872	-1.2710	-0.7759	-0.7590	-0.6741	0.9406	A56	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1303473
513	1.0846	-0.8905	0.4473	1.3676	-0.6741	0.9406	A299	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1587660
514	0.0714	-1.2710	-0.7836	-0.7590	2.6404	0.9406	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3463437
515	1.0863	0.6795	0.0422	-0.0501	2.6404	0.9406	A357	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3787651
516	1.8639	0.6795	0.4473	1.3676	-0.6741	0.9406	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3073320
517	0.3502	-0.0817	0.4473	-0.0501	-0.6741	0.9406	A204	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2573085
518	-0.4551	0.6795	0.0266	-0.7590	-0.6741	0.9406	A154	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2785563
519	-0.2213	-1.2710	0.0500	-0.0501	-0.6741	0.9406	A159	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1658280
520	0.7382	-1.2710	0.4473	1.3676	-0.6741	0.9406	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2032048
521	1.5089	0.6795	0.0422	-0.0501	-0.6741	0.9406	A251	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3146364
522	0.9114	-1.2710	0.4473	1.3676	-0.6741	0.9406	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1933160
523	-1.0370	-1.2710	-1.1420	-0.7590	0.4307	0.9406	A38	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3128437
524	0.3052	-0.8905	0.0500	-0.0501	-0.6741	0.9406	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1506105
525	-0.4828	-0.8905	-0.7836	-0.7590	-0.6741	0.9406	A88	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0956850
526	0.9547	0.6795	-0.2850	-0.0501	-0.6741	0.9406	A251	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2374682
527	0.6447	-1.2710	0.4473	1.3676	-0.6741	0.9406	A290	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2187881
528	2.4960	0.6795	0.0422	-0.0501	2.6404	0.9406	A394	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.2830720
529	0.7382	0.6795	0.0500	-0.0501	-0.6741	0.9406	A253	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1874864
530	0.7399	0.6795	0.0422	-0.0501	2.6404	0.9406	A336	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4364985
531	1.0413	-0.0817	-0.2850	-0.0501	0.4307	0.9406	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3263354
532	0.6776	-0.8905	-0.7836	-0.7590	2.6404	0.9406	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3292208
533	1.0066	0.6795	-0.2850	1.3676	0.4307	0.9406	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3262309
534	-1.0786	-0.8905	-0.2850	-0.0501	-0.6741	0.9406	A115	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3180783
535	0.9547	-0.0817	-0.2850	-0.0501	0.4307	0.9406	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3119021
536	-0.6473	-0.8905	-0.7759	-0.7590	-0.6741	0.9406	A72	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1143502
537	-1.0803	-0.8905	-0.7836	-0.7590	-0.6741	0.9406	A53	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1165450
538	0.3624	0.6795	0.0422	-0.0501	0.4307	0.9406	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1394515
539	0.8178	-0.8905	0.4473	1.3676	0.4307	0.9406	A323	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2417050
540	-1.5133	-1.2710	-1.1927	-1.1134	-0.6741	0.9406	A18	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1998862
541	1.1798	0.6795	-0.7836	-0.7590	2.6404	0.9406	A342	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.5312036
542	0.4871	-0.8905	-0.7836	-0.7590	2.6404	0.9406	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2974708
543	0.9997	0.6795	-0.2850	-0.0501	0.4307	0.9406	A280	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2508299
544	0.4801	0.6795	0.0500	-0.0501	-0.6741	0.9406	A239	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1444697
545	-0.7322	-0.0817	-0.2850	-0.7590	-0.6741	0.9406	A132	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2902504
546	0.0021	-0.8905	-0.2850	-0.7590	-0.6741	0.9406	A106	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2500271
547	0.1753	-0.8905	-0.2850	-0.7590	-0.6741	0.9406	A144	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2788937
548	0.9114	0.6795	0.0500	-0.0501	-0.6741	0.9406	A253	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2163530
549	0.7815	-0.0817	-0.2850	-0.0501	-0.6741	0.9406	A221	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2576465
550	1.3461	0.6795	0.4473	1.3676	-0.6741	0.9406	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2210320
551	0.2186	-0.8905	0.0500	-0.0501	-0.6741	0.9406	A189	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1361771
552	1.3461	0.6795	-0.2850	1.3676	0.4307	0.9406	A355	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2776372
553	-0.9054	-0.8905	-0.7759	-0.7590	-0.6741	0.9406	A72	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1469783
554	-0.9747	-0.0817	-0.2850	-0.7590	0.4307	0.6045	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2801483
555	-0.5590	0.6795	0.0461	-0.0501	0.4307	0.6045	A233	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2158258
556	-0.9054	0.6795	-1.1927	-1.1134	-0.6741	0.6045	A112	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2458856
557	0.6776	-0.8905	-0.7836	-0.7590	2.6404	0.6045	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2732041
558	-1.2518	-0.8905	-0.7836	-0.7590	-0.6741	0.6045	A63	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1167486
559	0.0818	0.6795	0.0461	-0.0501	2.6404	0.6045	A336	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3474315
560	-0.3789	-1.2710	-0.9473	-0.7590	-0.6741	0.6045	A84	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0926047
561	-0.3789	0.6795	0.1435	-0.7590	-0.6741	0.6045	A154	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2739974
562	-1.0786	-0.8905	-0.7759	-0.7590	-0.6741	0.6045	A63	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1302169
563	-0.5244	-0.8905	0.0461	-0.0501	0.4307	0.6045	A176	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1696800
564	0.2965	-0.8905	-1.1420	-0.7590	2.6404	0.6045	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2694208
565	-0.4741	-0.8905	-0.7759	-0.7590	-0.6741	0.6045	A90	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0424017
566	0.5044	-0.0817	-0.2850	-0.7590	0.4307	0.6045	A191	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2786334
567	0.5754	0.6795	-0.2850	-0.0501	0.4307	0.6045	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1411133
568	0.2186	-0.0817	0.0500	-0.0501	-0.6741	0.6045	A204	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1754310
569	-0.4741	-0.0817	-0.7759	-0.7590	-0.6741	0.6045	A120	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1371991
570	-0.7322	-0.0817	0.0266	-0.7590	-0.6741	0.6045	A132	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3054415
571	1.2577	0.6795	0.4473	1.3676	-0.6741	0.6045	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2027124
572	0.6447	-0.8905	0.4473	1.3676	-0.6741	0.6045	A264	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.0993548
573	1.4309	0.6795	0.4473	1.3676	-0.6741	0.6045	A353	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2101020
574	0.2983	-1.2710	0.4473	1.3676	0.4307	0.6045	A307	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3356884
575	0.7382	0.6795	0.0500	-0.0501	-0.6741	0.6045	A253	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1498922
576	-0.9747	0.6795	-0.2850	-0.7590	-0.6741	0.6045	A154	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2616232
577	-0.8985	-0.8905	-1.1420	-0.7590	-0.6741	0.6045	A59	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1310348
578	-0.5001	-0.8905	-0.7836	-0.7590	-0.6741	0.6045	A90	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0367850
579	-1.4336	-0.8905	-1.1927	-1.1134	0.4307	0.6045	A15	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3594860
580	-0.6906	-0.8905	-1.1420	-0.7590	-0.6741	0.6045	A75	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.0963848
581	0.4801	-0.8905	0.4473	1.3676	-0.6741	0.6045	A264	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1267881
582	-0.0394	-0.0817	0.0266	-0.0501	0.4307	0.6045	A238	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1945481
583	1.0846	-0.0817	0.4473	1.3676	-0.6741	0.6045	A314	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2150632
584	-1.0803	-0.8905	-0.2850	-0.7590	0.4307	0.6045	A141	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1812992
585	-0.9054	-0.8905	-0.7759	-0.7590	-0.6741	0.6045	A77	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1013502
586	-1.5981	-0.8905	-0.7759	-0.7590	-0.6741	0.6045	A54	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1694291
587	0.3589	0.6795	-0.7836	-0.0501	0.4307	0.6045	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1881300
588	-1.2518	-0.8905	-0.2850	-0.7590	-0.6741	0.6045	A89	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1998486
589	-0.0325	0.6795	-0.2850	-0.0501	-0.6741	0.6045	A216	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1821344
590	-1.2518	-0.0817	-1.1927	-1.1134	-0.6741	0.6045	A50	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3125402
591	-0.5070	-0.8905	-0.7836	-0.7590	0.4307	0.6045	A122	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.1203402
592	-0.3858	-0.0817	0.0422	-0.0501	-0.6741	0.6045	A204	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2321523
593	-0.9158	-1.2710	-0.7836	-0.7590	-0.6741	0.6045	A55	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1377519
594	-0.4205	-0.8905	0.0422	-0.0501	-0.6741	0.6045	A157	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1031356
595	1.1625	-0.8905	-0.2850	-0.7590	2.6404	0.6045	A311	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3007335
596	-1.7713	-1.2710	-0.7759	-0.7590	-0.6741	0.6045	A22	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2119648
597	0.8178	2.2969	0.0500	-0.0501	-0.6741	1.1422	A317	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2671646
598	-0.2144	1.1076	-1.1927	-1.1134	-0.6741	1.1422	A30	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3675021
599	1.5089	0.6795	0.0422	-0.0501	-0.6741	1.1422	A251	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3482364
600	1.3028	0.6795	0.0266	1.3676	0.4307	1.1422	A359	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2585309
601	0.1338	0.6795	0.0422	-0.0501	0.4307	1.1422	A259	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1723091
602	0.5650	1.1076	0.0500	-0.0501	-0.6741	1.1422	A239	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2635697
603	-0.3789	0.6795	0.1435	-0.7590	-0.6741	1.1422	A154	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3443396
604	1.1885	0.6795	-0.2850	-0.0501	2.6404	1.1422	A357	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4498651
605	0.0021	-0.8905	-0.2850	-0.7590	-0.6741	1.1422	A106	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2836271
606	-0.4828	-0.8905	-0.7836	-0.7590	-0.6741	1.1422	A88	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1292850
607	1.5262	2.2969	0.4434	1.3676	0.4307	1.1422	A396	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4122755
608	2.7385	0.6795	0.4434	-0.0501	2.6404	1.1422	A394	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4006205
609	1.0846	0.6795	0.4473	1.3676	-0.6741	1.1422	A338	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2902258
610	1.3530	2.2969	0.4434	1.3676	0.4307	1.1422	A396	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3834089
611	1.0846	2.2969	0.0500	-0.0501	-0.6741	1.1422	A317	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3116313
612	-0.6802	0.6795	0.0461	-0.0501	0.4307	1.1422	A234	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3086257
613	-0.2213	2.2969	-1.1927	-1.1134	0.4307	1.1422	A210	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.5828383
614	1.0066	-0.0817	-0.2850	-0.0501	0.4307	1.1422	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3541521
615	0.2186	-0.0817	0.0500	-0.0501	-0.6741	1.1422	A204	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2466252
616	0.6447	-0.0817	0.4473	1.3676	0.4307	1.1422	A347	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.4164689
617	-1.5133	-1.2710	-1.1927	-1.1134	-0.6741	1.1422	A8	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2334862
618	2.2189	2.2969	2.2860	1.3676	0.4307	1.1422	A402	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.8348255
619	0.7382	1.1076	0.0500	-0.0501	-0.6741	1.1422	A239	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2924364
620	-0.5677	0.6795	-0.7759	-0.7590	0.4307	1.1422	A173	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3299218
621	1.0413	-0.0817	-0.2850	-0.0501	0.4307	1.1422	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3599354
622	0.3589	-0.0817	-0.7836	-0.0501	2.6404	1.1422	A320	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3031959
623	1.3461	2.2969	0.4473	1.3676	-0.6741	1.1422	A396	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4207963
624	2.1150	0.6795	-0.2850	-0.0501	2.6404	1.1422	A385	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3077053
625	-0.3789	-0.8905	-0.7836	-0.7590	0.4307	1.1422	A100	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2199161
626	0.0541	0.6795	-0.7836	-0.7590	-0.6741	1.1422	A155	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2619896
627	0.4715	-0.8905	0.4473	1.3676	-0.6741	1.1422	A270	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2178381
628	2.0111	0.6795	0.4434	-0.0501	0.4307	1.1422	A329	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2319721
629	0.0039	-0.0817	0.4473	-0.0501	-0.6741	1.1422	A204	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3348186
630	-0.3945	2.2969	-0.2850	-0.7590	0.4307	1.1422	A210	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4013550
631	-1.0786	0.6795	-0.2850	-0.7590	-0.6741	1.1422	A102	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3492821
632	0.9114	2.2969	0.0500	-0.0501	-0.6741	1.1422	A317	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2827646
633	1.1625	-0.8905	-0.2850	-0.7590	2.6404	1.1422	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3903502
634	-1.5912	-0.8905	-0.2850	-0.7590	-0.6741	1.1422	A49	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2549089
635	-1.2518	-0.0817	-0.7759	-0.7590	-0.6741	1.1422	A51	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2576450
636	-0.7322	0.6795	-0.2850	-0.0501	-0.6741	1.1422	A154	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3699452
637	0.7382	-0.0817	0.4473	1.3676	-0.6741	1.1422	A312	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2857020
638	-0.9054	-0.0817	-0.2850	-0.0501	-0.6741	1.1422	A132	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4478570
639	0.4871	0.6795	0.4473	-0.0501	0.4307	1.1422	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2613515
640	-1.1929	-0.8905	-0.9473	-0.7590	-0.6741	1.1422	A53	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1165761
641	-1.0266	-0.8905	-0.6746	-0.7590	-0.6741	1.1422	A53	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1772617
642	0.1580	-0.8905	-1.1420	-0.7590	2.6404	1.1422	A1	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3618270
643	-0.3858	-0.0817	-0.7836	-0.0501	-0.6741	1.1422	A120	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3391246
644	-0.5677	1.1076	-1.1927	-1.1134	0.4307	1.1422	A68	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4789009
645	0.4715	-0.8905	0.4473	1.3676	0.4307	1.1422	A307	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3330217
646	-0.3010	-1.2710	0.0500	-0.0501	-0.6741	1.1422	A159	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2127113
647	0.0714	-0.0817	-0.2850	-0.7590	-0.6741	1.1422	A156	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3517127
648	-0.2473	0.6795	0.0461	-0.0501	0.4307	1.1422	A234	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2364757
649	-1.0716	-0.8905	-0.7914	-0.7590	-0.6741	1.5724	A25	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2219950
650	1.5781	0.6795	0.4434	-0.0501	-0.6741	1.5724	A300	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.4446793
651	0.7053	0.6795	0.4551	-0.0501	2.6404	1.5724	A336	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5307499
652	-0.7322	1.1076	-1.1927	-1.1134	-0.6741	1.5724	A30	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4304112
653	-0.7253	0.6795	-0.7836	-0.7590	-0.6741	1.5724	A131	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2789987
654	-0.9158	-1.2710	-0.7836	-0.7590	-0.6741	1.5724	A25	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2629307
655	-0.5677	-1.2710	0.0500	-0.0501	0.4307	1.5724	A161	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4022800
656	-0.9054	-0.0817	-0.2850	-0.0501	-0.6741	1.5724	A205	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.5195570
657	-0.0325	2.2969	-0.2850	-0.0501	-0.6741	1.5724	A283	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3226232
658	-0.3269	-0.8905	0.0266	-0.0501	-0.6741	1.5724	A153	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2514523
659	-0.5677	-1.2710	0.0500	-0.0501	-0.6741	1.5724	A135	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3288613
660	-0.3789	0.6795	-0.2850	-0.0501	0.4307	1.5724	A234	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3346207
661	0.8265	0.6795	0.4473	-0.0501	0.4307	1.5724	A267	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3896182
662	0.3139	0.6795	-0.7836	-0.7590	0.4307	1.5724	A173	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4057111
663	0.3918	-1.2710	0.4473	1.3676	-0.6741	1.5724	A270	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3662381
664	2.7385	0.6795	0.4434	-0.0501	2.6404	1.5724	A394	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4723205
665	-1.2518	-0.0817	-0.7759	-0.7590	-0.6741	1.5724	A103	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3293450
666	-1.0786	0.6795	-1.1927	-1.1134	-0.6741	1.5724	A17	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4167945
667	0.2273	2.2969	0.0266	-0.0501	0.4307	1.5724	A332	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3318298
668	-0.1278	-0.8905	0.0500	-0.0501	-0.6741	1.5724	A153	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2143689
669	-0.0394	2.2969	-0.7759	-0.7590	-0.6741	1.5724	A19	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4426330
670	1.0863	2.2969	0.4941	-0.0501	2.6404	1.5724	A395	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.7828228
671	2.2189	2.2969	2.2860	1.3676	0.4307	1.5724	A396	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.9065255
672	-1.2171	-0.8905	-0.7914	-0.7590	-0.6741	1.5724	A25	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1977450
673	0.5892	2.2969	0.0461	-0.0501	-0.6741	1.5724	A283	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3001146
674	0.3485	-0.8905	-0.7836	-0.7590	2.6404	1.5724	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3796708
675	0.5667	2.2969	0.4551	-0.0501	0.4307	1.5724	A332	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4175866
676	0.2792	2.2969	0.0266	-0.0501	-0.6741	1.5724	A283	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2701245
677	-1.0803	-0.8905	-0.2850	-0.7590	0.4307	1.5724	A12	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3426159
678	-0.3010	-1.2710	0.0500	-0.0501	-0.6741	1.5724	A153	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2844113
679	-0.5677	-1.2710	0.4473	-0.0501	-0.6741	1.5724	A135	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3948861
680	0.4784	-1.2710	0.4473	1.3676	-0.6741	1.5724	A270	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3518048
681	1.3357	0.6795	0.0422	-0.0501	-0.6741	1.5724	A240	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3910697
682	0.2169	0.6795	0.0500	-0.0501	-0.6741	1.5724	A240	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2695635
683	-0.0671	0.6795	0.0461	-0.0501	-0.6741	1.5724	A240	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3162468
684	-1.0786	-0.8905	-0.7759	-0.7590	-0.6741	1.5724	A25	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2234117
685	1.5781	2.2969	0.4434	1.3676	0.4307	1.5724	A396	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4926255
686	-1.0266	-0.8905	-0.6746	-0.7590	-0.6741	1.5724	A46	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2489617
687	-0.5070	-0.8905	-0.2850	-0.7590	2.6404	1.5724	A2	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4313398
688	1.5106	2.2969	0.4473	1.3676	0.4307	1.5724	A396	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4820255
689	-0.0498	-0.8905	-0.7836	-0.7590	0.4307	1.5724	A100	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3464661
690	0.9096	2.2969	0.0500	-0.0501	-0.6741	1.5724	A317	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3541646
691	-1.4180	-0.8905	-0.7914	-0.7590	-0.6741	1.5724	A25	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2133422
692	-0.3858	1.1076	-0.7759	-0.7590	-0.6741	1.5724	A134	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3330063
693	1.0932	0.6795	0.4434	1.3676	0.4307	1.5724	A362	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3219550
694	-0.8119	-0.8905	-0.7836	-0.7590	-0.6741	1.5724	A46	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2458002
695	-0.3945	-0.0817	0.0266	-0.7590	-0.6741	1.5724	A103	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4262579
696	0.6880	2.2969	-0.2850	-0.0501	2.6404	1.5724	A395	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.7352186
697	0.7745	2.2969	0.4941	-0.0501	0.4307	1.5724	A332	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4587199
698	0.0991	0.6795	0.4551	-0.0501	0.4307	1.5724	A267	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3186091
699	-0.0394	0.6795	0.0500	-0.0501	-0.6741	1.5724	A240	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3122801
700	1.0413	-0.0817	-0.2850	-0.0501	0.4307	1.5724	A252	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4316354
701	-0.4378	2.2969	-0.7914	-0.7590	-0.6741	1.5724	A19	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.5116163
702	-0.2213	2.2969	-1.1927	-1.1134	0.4307	1.5724	A210	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.6545383
703	-0.0481	-0.0817	0.4473	-0.0501	-0.6741	1.5724	A205	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4019104
704	-0.8534	-0.8905	-0.2850	-0.7590	2.6404	1.5724	A2	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4890731
705	-0.8205	0.6795	-1.1927	-1.1134	-0.6741	1.5724	A67	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3737779
706	0.7087	2.2969	0.4941	-0.0501	0.4307	1.5724	A332	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4477532
707	0.1251	0.6795	0.4473	-0.0501	-0.6741	1.5724	A240	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3510801
708	-1.0786	0.6795	-0.7759	-0.7590	-0.6741	1.5724	A102	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3391654
709	1.7513	0.6795	0.4434	-0.0501	-0.6741	1.5724	A300	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.4158126
710	0.9997	1.1076	0.4473	1.3676	-0.6741	1.5724	A363	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3440234
711	-0.3858	-0.8905	0.0461	-0.0501	0.4307	1.5724	A200	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3369058
712	-1.4249	-0.8905	-0.7759	-0.7590	-0.6741	1.5724	A25	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2170755
713	0.1753	-0.8905	-0.2850	-0.7590	-0.6741	1.5724	A96	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3841937
714	1.2318	0.6795	0.4434	1.3676	0.4307	1.5724	A362	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2988550
715	-0.9140	0.6795	-1.1927	-1.1134	-0.6741	1.5724	A67	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3893612
716	1.1279	0.6795	-0.2850	-0.0501	-0.6741	1.5724	A251	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3716348
717	-0.5850	2.2969	-0.7914	-0.7590	0.4307	1.5724	A210	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.5892050
718	-0.7409	-0.0817	-0.7759	-0.7590	0.4307	1.5724	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4358060
719	-0.2213	-0.8905	0.0500	-0.0501	0.4307	1.5724	A200	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3649725
720	-0.5607	-0.0817	0.0266	-0.7590	0.4307	1.5724	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4243983
721	-0.1295	-0.8905	0.0500	-0.0501	-0.6741	1.5724	A153	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2146523
722	-0.0481	2.2969	-0.7759	-0.7590	-0.6741	0.6851	A19	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3402276
723	-0.3789	-0.8905	0.4434	-0.7590	-0.6741	0.6851	A127	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2678103
724	-1.4527	-0.8905	-0.2850	-0.7590	-0.6741	0.6851	A89	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2135791
725	0.0437	0.6795	0.0500	-0.0501	-0.6741	0.6851	A216	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1505468
726	-0.2161	-0.8905	0.0500	-0.0501	-0.6741	0.6851	A157	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0812023
727	-1.0266	-0.8905	-0.6746	-0.7590	-0.6741	0.6851	A77	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1518669
728	-0.9071	-0.8905	-0.7759	-0.7590	-0.6741	0.6851	A77	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1150669
729	0.2203	-0.8905	0.4473	1.3676	-0.6741	0.6851	A264	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1835214
730	0.8265	0.6795	0.4941	1.3676	0.4307	0.6851	A348	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2310583
731	0.3069	2.2969	0.0500	-0.0501	-0.6741	0.6851	A279	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.1655525
732	-0.2646	-0.8905	0.4941	-0.0501	0.4307	0.6851	A213	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2838892
733	-0.2161	-0.8905	0.0500	-0.0501	-0.6741	0.6851	A157	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0812023
734	1.0066	0.6795	0.4434	1.3676	0.4307	0.6851	A359	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.1925916
735	1.5193	0.6795	2.2860	2.7854	-0.6741	0.6851	A417	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4310932
736	0.4871	0.6795	-0.2850	-0.7590	-0.6741	0.6851	A195	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1836073
737	-0.3079	-0.8905	0.0500	-0.0501	-0.6741	0.6851	A157	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.0965023
738	1.3530	2.2969	0.4473	1.3676	2.6404	0.6851	A424	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.5646824
739	-0.8985	-0.0817	-0.2850	-0.0501	0.4307	0.6851	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3067290
740	-1.0716	-0.8905	-0.7914	-0.7590	-0.6741	0.6851	A62	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1320486
741	-1.5912	-0.8905	-0.7914	-0.7590	-0.6741	0.6851	A54	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1522624
742	-0.1260	0.6795	0.4941	-0.0501	0.4307	0.6851	A266	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2147424
743	1.2595	-0.8905	2.2860	2.7854	-0.6741	0.6851	A404	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.5028795
744	0.5840	0.6795	0.4941	-0.0501	0.4307	0.6851	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2091182
745	0.3624	0.6795	0.4941	-0.0501	0.4307	0.6851	A266	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1721849
746	0.9114	0.6795	0.4473	1.3676	-0.6741	0.6851	A327	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2469957
747	-0.1537	0.6795	0.0266	-0.0501	-0.6741	0.6851	A216	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1795468
748	0.4871	2.2969	0.0578	1.3676	0.4307	0.6851	A392	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4705312
749	0.9997	2.2969	0.4473	1.3676	-0.6741	0.6851	A388	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3984433
750	-0.0325	0.6795	0.0266	-0.0501	-0.6741	0.6851	A216	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.1593468
751	-0.9920	-0.8905	-0.7759	-0.7590	-0.6741	0.6851	A77	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1292169
752	-0.8188	0.6795	0.0461	-0.0501	0.4307	0.6851	A233	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2555424
753	0.8144	-0.8905	-0.7836	-0.7590	2.6404	0.6851	A4	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3094374
754	0.3849	-0.8905	0.4473	1.3676	-0.6741	0.6851	A264	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1560881
755	1.2560	-0.8905	2.2860	2.7854	-0.6741	0.6851	A404	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.5034629
756	0.1407	0.6795	0.0578	1.3676	0.4307	0.6851	A341	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3338591
757	0.7226	0.6795	2.5976	-0.0501	0.4307	0.6851	A346	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.5828015
758	-0.0481	0.6795	0.0500	-0.0501	0.4307	0.6851	A247	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.1277424
759	-1.0786	0.6795	-0.7759	-0.7590	-0.6741	0.6851	A102	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.1912821
760	1.4309	-0.8905	2.2860	2.7854	-0.6741	0.6851	A404	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4743129
761	0.4715	2.2969	0.4473	-0.0501	-0.6741	0.6851	A279	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2435092
762	-0.7339	-0.8905	-0.2850	-0.7590	-0.6741	0.6851	A108	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1680169
763	-1.2448	-0.8905	-0.7914	-0.7590	0.4307	0.6851	A76	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2580402
764	0.6932	0.6795	0.0578	1.3676	2.6404	0.6851	A399	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.3810913
765	0.5494	-0.8905	2.5976	-0.0501	0.4307	0.6851	A346	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.7508125
766	0.0870	-0.8905	-0.7836	-0.7590	0.4307	0.6851	A122	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2213828
767	-1.4180	-0.0817	-0.2850	-0.7590	-0.6741	0.6851	A132	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3425958
768	-0.4759	-0.8905	0.0500	-0.0501	-0.6741	0.6851	A157	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1245023
769	-0.1728	-0.0817	-0.6356	-0.7590	2.6404	0.6851	A11	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.2477932
770	-1.4249	-0.8905	-0.7759	-0.7590	-0.6741	0.6851	A54	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1271291
771	0.4524	2.2969	0.0266	-0.0501	0.4307	0.6851	A324	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2232645
772	2.2034	2.2969	2.2860	2.7854	0.4307	0.6851	A431	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.7332650
773	-0.1260	-0.8905	0.4941	-0.0501	0.4307	0.6851	A213	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3069892
774	0.3225	-0.8905	0.4473	1.3676	-0.6741	0.6851	A264	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.1664881
775	-1.0266	-0.8905	0.0461	-0.0501	0.4307	0.6851	A176	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2668133
776	-0.8188	-0.8905	0.0461	-0.0501	0.4307	0.6851	A176	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2321800
777	1.2318	0.6795	0.4434	1.3676	0.4307	0.6851	A359	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.1550583
778	0.2186	2.2969	-0.7759	-0.7590	-0.6741	0.6851	A19	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.2957776
779	-0.5677	-0.8905	0.0500	-0.0501	-0.6741	0.6851	A157	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1398023
780	1.1798	0.6795	0.4434	1.3676	0.4307	0.6851	A359	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.1637250
781	0.5321	0.6795	0.4941	1.3676	0.4307	0.6851	A348	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.2801250
782	-0.3858	0.6795	-0.2850	-0.7590	-0.6741	0.0398	A174	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2955474
783	-0.5590	0.6795	0.0461	-0.0501	-0.6741	0.0398	A225	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3464030
784	-1.0872	-0.8905	0.0500	-0.7590	0.4307	0.0398	A148	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3314632
785	-0.2057	0.6795	0.0266	-0.0501	-0.6741	0.0398	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2873193
786	-0.5677	0.6795	0.0500	-0.7590	0.4307	0.0398	A194	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3928237
787	-1.7713	-1.2710	-0.7759	-0.7590	-0.6741	0.0398	A37	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3060814
788	0.5823	0.6795	0.4473	1.3676	-0.6741	0.0398	A327	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4093957
789	-0.5070	0.6795	0.0461	-0.0501	0.4307	0.0398	A254	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3012758
790	0.6602	2.2969	0.4434	-0.0501	0.4307	0.0398	A334	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4349145
791	0.7364	0.6795	0.4473	1.3676	-0.6741	0.0398	A327	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3837124
792	0.6533	0.6795	0.4473	-0.0501	0.4307	0.0398	A273	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3105683
793	0.5633	2.2969	0.0500	-0.0501	-0.6741	0.0398	A279	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3001426
794	-0.0481	2.2969	0.0500	-0.7590	0.4307	0.0398	A334	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4223676
795	1.7756	0.6795	2.2860	2.7854	-0.6741	0.0398	A417	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3662599
796	-0.0481	0.6795	0.0500	-0.0501	0.4307	0.0398	A273	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2254425
797	-1.6103	-0.8905	-0.2850	-0.7590	0.4307	0.0398	A139	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3628132
798	-1.1582	-0.8905	-0.7914	-0.7590	0.4307	0.0398	A86	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3176810
799	-1.4527	-0.8905	-0.2850	-0.7590	-0.6741	0.0398	A81	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2785384
800	-0.6300	-0.8905	0.0500	-0.0501	-0.6741	0.0398	A171	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2083285
801	-0.5677	0.6795	0.0500	-0.7590	-0.6741	0.0398	A174	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3437398
802	3.6893	2.2969	1.2264	-0.0501	2.6404	0.0398	A434	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.9264293
803	0.6447	-0.8905	2.2860	1.3676	-0.6741	0.0398	A313	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.4718242
804	-0.5521	0.6795	0.0266	-0.0501	-0.6741	0.0398	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3450526
805	-0.6924	-0.0817	-0.2850	-0.7590	2.6404	0.0398	A235	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3832243
806	-0.2923	0.6795	0.0266	-0.0501	0.4307	0.0398	A254	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2622425
807	-0.7253	0.6795	-0.2850	-0.0501	0.4307	0.0398	A254	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3421708
808	-1.2448	-0.8905	-0.7914	-0.7590	-0.6741	0.0398	A65	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1899590
809	0.2186	0.6795	0.0500	-0.0501	-0.6741	0.0398	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2205026
810	1.6838	2.2969	2.2860	1.3676	-0.6741	0.0398	A431	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.8045939
811	1.7150	0.6795	-0.5577	-0.7590	0.4307	0.0398	A302	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.4205684
812	-1.4527	-1.2710	-0.7836	-0.7590	-0.6741	0.0398	A37	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2516981
813	0.3918	-0.8905	0.4473	1.3676	-0.6741	0.0398	A278	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2075242
814	0.2273	2.2969	0.0266	-0.0501	0.4307	0.0398	A334	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3355245
815	-0.6283	0.6795	0.0461	-0.0501	0.4307	0.0398	A254	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3214925
816	2.2034	2.2969	2.2860	2.7854	-0.6741	0.0398	A431	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.7071265
817	0.4005	2.2969	0.4434	-0.0501	0.4307	0.0398	A334	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.3916312
818	1.2595	-0.8905	2.2860	2.7854	-0.6741	0.0398	A404	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3953295
819	2.2034	2.2969	2.2860	2.7854	0.4307	0.0398	A431	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.6257150
820	1.3339	-0.8905	-0.5577	-0.7590	2.6404	0.0398	A311	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.4358225
821	-1.4180	-0.0817	-0.7914	-0.7590	-0.6741	0.0398	A94	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3208857
822	-0.3010	0.6795	0.0500	-0.7590	-0.6741	0.0398	A174	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3655140
823	1.6924	0.6795	2.5976	1.3676	2.6404	0.0398	A409	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.6859729
824	-0.5677	-0.8905	0.0500	-0.0501	-0.6741	0.0398	A171	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.1979452
825	2.4701	2.2969	2.2860	2.7854	-0.6741	0.0398	A431	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.7515765
826	0.2169	-0.8905	0.4473	1.3676	-0.6741	0.0398	A278	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2366742
827	-0.3460	-0.0817	-0.2850	-0.0501	2.6404	0.0398	A235	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3275666
828	1.6214	0.6795	2.2860	2.7854	-0.6741	0.0398	A417	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3405599
829	0.3849	-0.8905	0.4473	1.3676	0.4307	0.0398	A315	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.3238578
830	-1.3384	-0.0817	-0.7759	-0.7590	-0.6741	0.3490	A94	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.3035291
831	-0.9850	0.6795	-0.7836	-0.7590	0.4307	0.3490	A194	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3704296
832	1.3339	-0.8905	-0.5577	-0.7590	2.6404	0.3490	A311	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3842891
833	-0.3512	0.6795	0.4941	-0.0501	0.4307	0.3490	A266	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2984425
834	0.7364	0.6795	1.1953	1.3676	-0.6741	0.3490	A327	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4568457
835	-0.4378	0.6795	0.4941	-0.0501	-0.6741	0.3490	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3523860
836	-1.1600	-0.8905	-0.2850	-0.7590	-0.6741	0.3490	A89	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.2553697
837	2.3315	0.6795	-0.1915	-0.0501	0.4307	0.3490	A302	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.2562049
838	-0.5070	0.6795	0.0461	-0.0501	0.4307	0.3490	A233	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2497425
839	-0.2057	0.6795	0.0266	-0.0501	-0.6741	0.3490	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2357860
840	-0.3789	0.6795	0.0266	-0.0501	-0.6741	0.3490	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2646526
841	-1.4180	0.6795	-0.2850	-0.7590	-0.6741	0.3490	A174	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3780898
842	0.9114	0.6795	1.1953	1.3676	-0.6741	0.3490	A327	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4276791
843	-0.3806	0.6795	-0.2850	-0.0501	0.4307	0.3490	A233	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2331875
844	-1.2448	-0.8905	-0.2850	-0.7590	-0.6741	0.3490	A89	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2435986
845	1.4240	-0.8905	2.2860	2.7854	0.4307	0.3490	A411	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3380347
846	-1.2448	-0.8905	-0.7914	-0.7590	-0.6741	0.3490	A65	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.1591986
847	0.5633	-0.8905	1.1953	1.3676	-0.6741	0.3490	A313	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2520742
848	0.5823	0.6795	1.1953	1.3676	-0.6741	0.3490	A327	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4825291
849	1.6907	-0.8905	2.2860	2.7854	-0.6741	0.3490	A404	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4448739
850	-0.4464	0.6795	0.0461	-0.0501	0.4307	0.3490	A233	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2396425
851	-1.7644	-0.8905	-0.7914	-0.7590	-0.6741	0.3490	A54	C512	-1.2707584	-1.0397430	-0.9098567	-0.8315183	-0.5949957	0.8589108	0.2371458
852	1.0776	2.2969	1.1953	1.3676	-0.6741	0.3490	A388	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.5661433
853	0.9044	2.2969	2.2860	-0.0501	-0.6741	0.3490	A279	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.6781259
854	-0.2750	0.6795	0.0461	-0.0501	0.4307	0.3490	A233	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2110758
855	-0.9920	-0.8905	-0.7759	-0.7590	-0.6741	0.3490	A92	C471	-0.5129045	-0.9728948	-0.8664462	-0.7547916	-0.6741149	0.5660586	0.1455531
856	-1.4249	-1.2710	-0.2850	-0.0501	-0.6741	0.3490	A115	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3862876
857	0.5667	0.6795	0.4473	-0.0501	0.4307	0.3490	A277	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.2446016
858	0.6602	0.6795	0.4434	1.3676	0.4307	0.3490	A348	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.3063416
859	-1.2985	-0.8905	-0.2850	-0.7590	0.4307	0.3490	A76	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2593132
860	-0.3858	2.2969	-0.7759	-0.7590	-0.6741	0.3490	A19	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.4317640
861	-0.3789	0.6795	0.0266	-0.0501	-0.6741	0.3490	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2646526
862	1.3443	2.2969	1.1953	1.3676	-0.6741	0.3490	A388	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.5290829
863	0.8144	-0.8905	-0.5577	-0.7590	0.4307	0.3490	A207	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3331801
864	-0.2819	0.6795	1.2342	-0.0501	-0.6741	0.3490	A289	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.4497526
865	1.4309	-0.8905	2.2860	2.7854	-0.6741	0.3490	A404	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4182962
866	-0.2836	2.2969	-0.7759	-0.7590	-0.6741	0.3490	A19	C301	0.3539798	2.2969425	-0.1275540	-0.2934206	-0.2808659	0.8171839	0.4354943
867	1.5262	0.6795	2.2860	1.3676	0.4307	0.3490	A402	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.4788823
868	-0.7253	0.6795	-0.2850	-0.0501	-0.6741	0.3490	A184	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3401844
869	0.2186	0.6795	0.4473	-0.0501	-0.6741	0.3490	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2351860
870	-0.3858	0.6795	0.0461	-0.0501	-0.6741	0.3490	A225	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.2690526
871	0.3069	-0.8905	1.1953	1.3676	-0.6741	0.3490	A313	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.2948075
872	-1.2985	-0.0817	-0.2850	-0.7590	0.4307	0.3490	A149	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3757622
873	2.1947	0.6795	6.5710	1.3676	0.4307	0.3490	A438	B12	5.5095223	0.6794579	3.0650965	4.2031619	2.6404120	0.4969247	2.0023277
874	-0.8829	0.6795	-0.2850	-0.7590	-0.6741	0.3490	A174	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.2889065
875	-0.4135	-0.8905	0.4473	-0.0501	0.4307	0.3490	A213	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.2794865
876	1.0066	0.6795	0.4434	1.3676	2.6404	-0.2290	A399	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.4860395
877	-0.8274	-0.8905	-0.7759	-0.7590	-0.6741	-0.2290	A93	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1915128
878	-0.8898	-0.8905	-0.7759	-0.7590	-0.6741	-0.2290	A93	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1811128
879	-0.7322	-0.8905	0.4473	-0.0501	-0.6741	-0.2290	A198	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3361867
880	-0.7166	-0.0817	-0.7759	-0.7590	-0.6741	-0.2290	A113	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3080082
881	-1.4249	-0.0817	-0.7759	-0.7590	-0.6741	-0.2290	A116	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2746523
882	0.4801	0.6795	0.4473	-0.0501	-0.6741	-0.2290	A260	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3120089
883	0.0385	-0.8905	2.2860	-0.0501	-0.6741	-0.2290	A258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.5389949
884	0.2983	0.6795	0.4473	-0.0501	-0.6741	-0.2290	A260	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3182360
885	-0.6750	0.6795	0.0461	-0.0501	-0.6741	-0.2290	A232	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2822696
886	-1.2448	-0.8905	-0.2850	-0.7590	-0.6741	-0.2290	A81	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1990884
887	0.5650	-0.8905	1.1953	1.3676	0.4307	-0.2290	A352	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.4323136
888	-1.3332	-0.8905	-0.2850	-0.7590	0.4307	-0.2290	A148	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3477399
889	-0.2057	0.6795	0.0266	-0.0501	-0.6741	-0.2290	A232	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3321193
890	-0.5521	0.6795	0.0266	-0.0501	-0.6741	-0.2290	A232	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3060030
891	0.1389	-0.8905	0.4473	-0.0501	2.6404	-0.2290	A249	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3935002
892	-1.0716	0.6795	-0.2850	-0.0501	0.4307	-0.2290	A245	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3874998
893	-0.0412	-0.8905	0.4473	-0.0501	-0.6741	-0.2290	A198	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2210200
894	-1.1253	-0.8905	-0.6356	-1.1134	2.6404	-0.2290	A13	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.5964454
895	-1.6103	-0.8905	-0.2850	-0.7590	0.4307	-0.2290	A139	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3939233
896	-0.9504	0.6795	0.0461	-0.0501	-0.6741	-0.2290	A232	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2959476
897	-1.7644	-0.8905	-0.7914	-0.7590	-0.6741	-0.2290	A52	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2317590
898	-1.4180	0.6795	-0.2850	-0.7590	-0.6741	-0.2290	A129	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4568818
899	1.1642	0.6795	1.1953	1.3676	0.4307	-0.2290	A378	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4439916
900	-0.5019	0.6795	0.0461	-0.0501	-0.6741	-0.2290	A232	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3111196
901	0.1338	-0.8905	1.1953	1.3676	0.4307	-0.2290	A351	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.4100646
902	-0.1347	-0.0817	0.4473	-0.0501	0.4307	-0.2290	A242	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.4194647
903	-0.0325	0.6795	0.4473	-0.0501	0.4307	-0.2290	A287	C297	0.2458726	0.5046167	-0.1059150	-0.1622064	0.4307274	0.6555503	0.3338592
904	-1.0872	-0.8905	-0.7759	-0.7590	-0.6741	-0.2290	A93	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1482128
905	-0.7322	0.6795	-0.2850	-0.7590	-0.6741	-0.2290	A174	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3601232
906	1.9592	2.2969	1.2264	-0.0501	-0.6741	-0.2290	A393	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.7509662
907	1.5348	-0.8905	2.2860	2.7854	0.4307	-0.2290	A416	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.2451414
908	2.9619	2.2969	1.2264	-0.0501	0.4307	-0.2290	A393	C219	1.9755430	0.5864499	0.2031375	-0.2014525	0.1203785	0.5809108	0.8319424
909	1.0776	-0.8905	1.1953	1.3676	2.6404	-0.2290	A401	C228	0.8544662	-0.8230415	0.4195786	1.3676416	-0.4672508	0.5202082	0.8205357
910	-0.6543	-0.0817	-0.7759	-0.7590	0.4307	-0.2290	A164	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.3980366
911	-1.9376	-0.8905	-0.2850	-0.7590	-0.6741	-0.2290	A81	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3145551
912	-1.2448	-0.8905	-0.7914	-0.7590	-0.6741	-0.2290	A66	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1451590
913	-0.8829	0.6795	0.4941	-0.7590	-0.6741	-0.2290	A182	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3434682
914	1.0863	-0.8905	2.2860	2.7854	0.4307	-0.2290	A416	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3019804
915	-0.2421	-0.8905	-0.2850	-0.7590	2.6404	-0.2290	A13	C360	0.2356188	-0.4603485	-0.3744382	-0.4107768	2.6404120	0.5948051	0.3615581
916	0.5477	0.6795	1.2342	-0.0501	2.6404	-0.2290	A387	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.3253056
917	-1.4249	-1.2710	-0.2850	-0.0501	-0.6741	-0.2290	A124	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.4095972
918	-0.2057	-0.8905	-0.7836	-0.0501	-0.6741	-0.2290	A133	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3213452
919	3.6893	2.2969	1.2264	-0.0501	2.6404	-0.2290	A434	C96	2.0782294	0.7478725	0.1122044	0.7127714	2.6404120	0.5611990	0.9712293
920	-1.1496	-0.8905	-0.7759	-0.7590	-0.6741	-0.2290	A93	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1378128
921	-0.4724	0.6795	-0.2850	-0.7590	-0.6741	-0.2290	A174	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3259140
922	-0.5677	-0.8905	0.4473	-0.0501	-0.6741	-0.8071	A186	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4051200
923	-0.6906	-0.8905	-0.5577	-0.7590	-0.6741	-0.8071	A74	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1665373
924	-0.3079	-0.0817	0.4473	-0.0501	0.4307	-0.8071	A242	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3429258
925	0.0385	-0.8905	2.2860	-0.0501	-0.6741	-0.8071	A258	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.6353449
926	0.0385	-0.0817	2.2860	-0.0501	-0.6741	-0.8071	A319	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.5137818
927	-0.9054	0.6795	-0.7759	-0.7590	-0.6741	-0.8071	A150	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3486013
928	0.6499	-0.0817	1.1953	1.3676	0.4307	-0.8071	A364	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2784382
929	-0.3702	-0.8905	0.4473	-0.0501	-0.6741	-0.8071	A198	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3722033
930	-1.0786	-0.8905	-0.2850	-0.0501	-0.6741	-0.8071	A124	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2643961
931	-1.5046	-0.8905	-0.5577	-0.7590	-0.6741	-0.8071	A41	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1381002
932	0.3139	0.6795	0.4434	1.3676	0.4307	-0.8071	A372	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.4496261
933	-0.7253	0.6795	0.0266	-0.0501	-0.6741	-0.8071	A224	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.1874424
934	0.6602	0.6795	0.4434	1.3676	0.4307	-0.8071	A376	C147	1.3221312	0.9897072	0.3646278	1.3676416	-0.0080377	0.6973599	0.4990250
935	-1.2448	-0.0817	0.5136	-0.7590	0.4307	-0.8071	A206	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4161137
936	1.6024	-0.8905	2.2860	2.7854	0.4307	-0.8071	A419	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3527581
937	-0.2213	0.6795	0.4473	-0.0501	-0.6741	-0.8071	A246	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2585864
938	-0.3079	-0.8905	0.4473	-0.0501	-0.6741	-0.8071	A198	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3618200
939	-0.3789	0.6795	0.0266	-0.0501	-0.6741	-0.8071	A224	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2451757
940	-0.4655	0.6795	0.0266	-0.0501	0.4307	-0.8071	A276	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2469113
941	0.1251	0.6795	2.2860	-0.0501	-0.6741	-0.8071	A319	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.6012197
942	1.4327	0.6795	2.2860	2.7854	0.4307	-0.8071	A421	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.2993663
943	-0.7253	0.6795	0.5136	-0.0501	0.4307	-0.8071	A272	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2018053
944	0.1563	0.6795	1.2342	-0.0501	0.4307	-0.8071	A303	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.4448777
945	-0.6300	-0.8905	0.4473	-0.0501	-0.6741	-0.8071	A186	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4078736
946	-0.7253	0.6795	-0.7836	-0.0501	-0.6741	-0.8071	A150	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3224757
947	-1.5912	-0.8905	-0.7914	-0.7590	-0.6741	-0.8071	A41	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1440541
948	0.6499	-0.8905	0.4473	-0.0501	-0.6741	-0.8071	A217	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.4307949
949	-0.5677	-0.8905	0.4473	-0.0501	0.4307	-0.8071	A227	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.4344258
950	-0.1260	0.6795	0.0266	-0.0501	0.4307	-0.8071	A276	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3034946
951	-0.0412	-0.8905	0.4473	-0.0501	-0.6741	-0.8071	A198	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3173700
952	0.2983	-0.8905	1.1953	1.3676	0.4307	-0.8071	A358	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3546382
953	-1.5046	-0.8905	-0.7914	-0.7590	0.4307	-0.8071	A86	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2635390
954	0.4957	-0.8905	1.1953	1.3676	0.4307	-0.8071	A361	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3875382
955	-0.9920	0.6795	-0.2850	-0.0501	-0.6741	-0.8071	A224	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2683704
956	0.5477	0.6795	1.2342	-0.0501	2.6404	-0.8071	A387	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.2289556
957	1.5972	0.6795	2.2860	2.7854	0.4307	-0.8071	A421	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3106607
958	-0.8032	0.6795	-0.7759	-0.7590	-0.6741	-0.8071	A150	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3656346
959	0.1493	-0.8905	1.1953	1.3676	0.4307	-0.4173	A351	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3760979
960	0.3849	-0.8905	1.1953	1.3676	0.4307	-0.4173	A351	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3709136
961	1.4292	0.6795	2.2860	2.7854	0.4307	-0.4173	A421	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.2349830
962	-1.1652	-0.8905	-0.7759	-0.7590	-0.6741	-0.4173	A66	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1038294
963	-0.6543	-0.8905	0.4473	-0.0501	0.4307	-0.4173	A227	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4275606
964	-0.9504	0.6795	0.0461	-0.0501	-0.6741	-0.4173	A224	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2645643
965	0.2013	0.6795	1.2342	-0.0501	2.6404	-0.4173	A387	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.2361890
966	0.5633	0.6795	1.1953	1.3676	0.4307	-0.4173	A376	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3677182
967	-0.2144	-0.8905	0.4473	-0.0501	-0.6741	-0.4173	A198	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.2812700
968	-1.1738	-0.8905	-0.7759	-0.7590	-0.6741	-0.4173	A66	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1023961
969	-1.0006	-0.8905	-0.7759	-0.7590	-0.6741	-0.4173	A93	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1312628
970	-0.5590	0.6795	0.4473	-0.0501	-0.6741	-0.4173	A246	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2606137
971	-0.5192	0.6795	0.4941	-0.0501	-0.6741	-0.4173	A246	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2750470
972	-0.2750	0.6795	2.2860	-0.0501	0.4307	-0.4173	A339	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.5592589
973	-0.6231	-0.0817	0.4941	-0.0501	0.4307	-0.4173	A242	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3565030
974	-1.0006	-0.0817	-0.7759	-0.7590	-0.6741	-0.4173	A116	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2333228
975	0.3035	-0.8905	1.1953	1.3676	0.4307	-0.4173	A351	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3573469
976	0.0541	0.6795	-0.5577	-0.0501	-0.6741	-0.4173	A229	C326	0.4078813	0.4460352	-0.0758046	-0.2045827	-0.6741149	0.6597676	0.3834511
977	1.1694	-0.8905	2.2860	2.7854	0.4307	-0.4173	A416	C55	1.4810668	0.0181922	2.3175726	2.6287019	0.1225346	-0.2170130	0.3195137
978	-0.3616	0.6795	-0.5577	-0.7590	-0.6741	-0.4173	A146	C434	-0.4996726	0.3958051	-0.8430922	-0.8445203	-0.5645193	0.6623233	0.3303140
979	-0.5365	-0.0817	0.4941	-0.0501	0.4307	-0.4173	A242	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3709364
980	-0.0862	0.6795	2.2860	-0.0501	-0.6741	-0.4173	A319	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.6382785
981	-1.3332	-0.8905	-0.7836	-0.7590	-0.6741	-0.4173	A66	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1272090
982	0.3225	-0.0817	1.1953	1.3676	0.4307	-0.4173	A364	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2257136
983	-1.0630	-0.8905	-0.7759	-0.7590	-0.6741	-0.4173	A93	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.1208628
984	0.8334	2.2969	1.2342	1.3676	0.4307	-0.4173	A403	C57	0.1798241	1.6748331	2.0467757	1.3131124	0.3882335	-1.9684654	0.6227230
985	-0.4811	-0.0817	0.4473	-0.0501	0.4307	-0.4173	A242	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.3723697
986	-1.0734	-0.8905	0.0422	-0.7590	-0.6741	-0.4173	A111	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2301755
987	-1.4180	-0.8905	0.0422	-0.7590	-0.6741	-0.4173	A111	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2511051
988	-0.1884	0.6795	0.0461	-0.0501	2.6404	-0.4173	A344	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.2254486
989	-1.0872	-0.8905	-0.7759	-0.7590	-0.6741	-0.5382	A66	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.0966794
990	-0.5677	-0.8905	0.4473	-0.0501	-0.6741	-0.5382	A198	C384	-0.0359246	-0.9572228	0.0505756	-0.1222502	-0.6547317	0.5368788	0.3603033
991	0.3554	-0.0817	1.1953	1.3676	0.4307	-0.5382	A364	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2110469
992	0.1338	-0.0817	1.1953	1.3676	0.4307	-0.5382	A364	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.2237312
993	0.0627	-0.8905	1.1953	1.3676	0.4307	-0.5382	A351	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3703812
994	-0.0394	-0.8905	1.1953	1.3676	0.4307	-0.5382	A351	C130	0.2826529	-0.0067137	1.6381330	0.9923522	0.2097590	-0.6177263	0.3873979
995	-0.4239	0.6795	0.4473	-0.0501	-0.6741	-0.5382	A246	C306	-0.7716661	0.5467115	0.2755179	-0.2228202	-0.1702066	-0.7871317	0.2629803
996	-1.5219	-0.8905	0.0422	-0.7590	-0.6741	-0.5382	A111	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.2482717
997	-0.5677	-0.8905	0.4473	-0.0501	0.4307	-0.5382	A227	C414	-0.4728276	-0.8354472	-0.4305269	-0.4820923	0.4307274	0.6016920	0.4332773
998	-1.0006	0.6795	-0.7759	-0.7590	-0.6741	-0.5382	A129	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3400395
999	-1.0214	-0.0817	0.0578	-0.7590	2.6404	-0.5382	A295	C142	0.1328959	0.6627507	0.4814828	-0.0248014	2.6404120	-0.9712527	0.5816155
1000	-1.0630	0.6795	-0.7759	-0.7590	-0.6741	-0.5382	A129	C478	-1.1829114	-0.7922801	-0.6999883	-0.7557764	-0.5234546	-0.6945646	0.3296395


hist(sorted_df$diff, breaks = 30, col = "blue", main = "Mean Absolute Difference", xlab = "Difference")

Figure 22: Mean Absolute Difference

muHVT: Predicting Cells with Layers using predictLayerHVT

Zubin Dowlaty, Srinivasan Sudarsanam, Somya Shambhawi

2023-07-03

1 Abstract

2 Example: muHVT with the Personal Computer dataset

3 Map A : Base Compressed Map

3.0.1 Iteration 1:

3.0.2 Iteration 2:

4 Map B : Compressed Novelty Map

4.1 Voronoi Tessellation with highlighted novelty cell

5 Map C : Compressed Map without Novelty

5.0.1 Iteration 1:

5.0.2 Iteration 2:

6 Prediction on Test Data

7 Executive Summary

8 References